From a0bd11747f5f49480efaa82f880059625578ae8f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 09:04:37 -0500 Subject: [PATCH 001/118] add commented structure to get started --- _pkgdown.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/_pkgdown.yml b/_pkgdown.yml index c72331d3..23c01a59 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,3 +1,13 @@ template: params: bootswatch: flatly +# reference: +# - title: Layers +# - subtitle: Geoms +# desc: Geom is short for geometric element +# - contents: +# - starts_with("geom") +# - subtitle: Stats +# desc: Statistical transformations transform data before display. +# contents: +# - starts_with("stat") From 2deeb3db8c32dade9f9825e8bc0bbb44a9b84f3a Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 09:59:12 -0500 Subject: [PATCH 002/118] add reference structure --- _pkgdown.yml | 57 +++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 47 insertions(+), 10 deletions(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 23c01a59..345c222e 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,13 +1,50 @@ template: params: bootswatch: flatly -# reference: -# - title: Layers -# - subtitle: Geoms -# desc: Geom is short for geometric element -# - contents: -# - starts_with("geom") -# - subtitle: Stats -# desc: Statistical transformations transform data before display. -# contents: -# - starts_with("stat") + +reference: + +- title: Workflow steps + +- subtitle: Casting boundaries + desc: Create a spatial boundaries object for your model +- contents: + - spm_as_boundary + +- subtitle: Discretizing boundaries + desc: Discretize your spatial boundaries object into patches +- contents: + - spm_discretize + +- subtitle: Casting datasets + desc: Create dataset objects for your raw variables +- contents: + - spm_as_dataset + +- subtitle: Smoothing biomass + desc: Smooth the raw variables using gams +- contents: + - spm_smooth + +- subtitle: Aggregating catch + desc: Aggregate the catch data according to discretized boundaries +- contents: + - spm_aggregate_catch + +- subtitle: Assembling and modifiying SSPM objects + desc: Assemble the model object and prepare it for fitting +- contents: + - sspm + - spm_split + - spm_lag + +- subtitle: Fitting the surplus production model + desc: Fit the model +- contents: + - fit + +- subtitle: Predict and plot + desc: Predict and plot results +- contents: + - predict + - plot From 960d43ba668120b745a0310fcd0513053fd7ea88 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 10:55:31 -0500 Subject: [PATCH 003/118] update roxygen --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 71b22666..548e1003 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,7 @@ BugReports: https://github.com/pedersen-fisheries-lab/sspm/issues URL: https://pedersen-fisheries-lab.github.io/sspm/ Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.1 +RoxygenNote: 7.2.2 Depends: sf, mgcv, From ea6792e71d9cf646efc1378e34c29a5eba3cd76a Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 10:56:03 -0500 Subject: [PATCH 004/118] add internal keywords to some methods --- R/fit.R | 3 +++ R/map_formula.R | 2 ++ man/fit.Rd | 1 + man/map_formula.Rd | 1 + 4 files changed, 7 insertions(+) diff --git a/R/fit.R b/R/fit.R index bc08d4b6..c2729cef 100644 --- a/R/fit.R +++ b/R/fit.R @@ -12,6 +12,8 @@ #' @return #' A object of the class [`sspm_fit`][sspm_fit-class]. #' +#' @keywords internal +#' # Examples # \dontrun{ # fit_smooths(boundaries = boundaries, keep_fit = TRUE, ...) @@ -48,6 +50,7 @@ setGeneric(name = "fit_spm", # Methods ----------------------------------------------------------------- #' @rdname fit +#' @keywords internal setMethod(f = "fit_smooths", signature(sspm_object = "sspm_dataset", boundaries = "sspm_discrete_boundary"), diff --git a/R/map_formula.R b/R/map_formula.R index 43945fcc..6e614921 100644 --- a/R/map_formula.R +++ b/R/map_formula.R @@ -9,6 +9,8 @@ #' @return #' The updated object. #' +#' @keywords internal +#' # Examples # \dontrun{ # map_formula(data_frame = all_data, boundaries = boundaries, diff --git a/man/fit.Rd b/man/fit.Rd index ff668ab8..1acb3968 100644 --- a/man/fit.Rd +++ b/man/fit.Rd @@ -179,3 +179,4 @@ A object of the class \code{\link[=sspm_fit-class]{sspm_fit}}. Once formulas have been mapped onto a sspm discrete object, the GAMs can be fitted with this function. Arguments can be passed onto \code{bam}. } +\keyword{internal} diff --git a/man/map_formula.Rd b/man/map_formula.Rd index a5184e8b..fefacaf9 100644 --- a/man/map_formula.Rd +++ b/man/map_formula.Rd @@ -71,3 +71,4 @@ The updated object. This functions is now used internally to map a formula onto a \code{sspm_dataset} or \code{sspm} object. } +\keyword{internal} From a104838688c5e99035c9e211b5d2d0eddb61cf02 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 10:56:12 -0500 Subject: [PATCH 005/118] add alias for extract --- R/methods-extract.R | 1 + man/extract-methods.Rd | 1 + 2 files changed, 2 insertions(+) diff --git a/R/methods-extract.R b/R/methods-extract.R index 3de79411..cf671c7c 100644 --- a/R/methods-extract.R +++ b/R/methods-extract.R @@ -15,6 +15,7 @@ #' } #' #' @export +#' @aliases extract #' @rdname extract-methods setMethod("$", "sspm_boundary", diff --git a/man/extract-methods.Rd b/man/extract-methods.Rd index ec0c6fec..bfe03e0c 100644 --- a/man/extract-methods.Rd +++ b/man/extract-methods.Rd @@ -2,6 +2,7 @@ % Please edit documentation in R/methods-extract.R \name{$,sspm_boundary-method} \alias{$,sspm_boundary-method} +\alias{extract} \alias{$,sspm_discrete_boundary-method} \alias{$,sspm_dataset-method} \alias{$,sspm-method} From aabafb0940f6ee96edcbaae875c1908e0a190756 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 10:57:07 -0500 Subject: [PATCH 006/118] organize reference for website, closes #56 --- _pkgdown.yml | 39 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 345c222e..e23f88a3 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -10,41 +10,78 @@ reference: desc: Create a spatial boundaries object for your model - contents: - spm_as_boundary + - sspm_boundary-class - subtitle: Discretizing boundaries desc: Discretize your spatial boundaries object into patches - contents: - spm_discretize + - sspm_discrete_boundary-class + - tesselate_voronoi + - triangulate_delaunay + - spm_methods + - as_discretization_method + - discretization_method-class - subtitle: Casting datasets desc: Create dataset objects for your raw variables - contents: - spm_as_dataset + - sspm_dataset-class - subtitle: Smoothing biomass desc: Smooth the raw variables using gams - contents: - spm_smooth + - spm_smooth_methods + - smooth_time + - smooth_space + - smooth_space_time - subtitle: Aggregating catch desc: Aggregate the catch data according to discretized boundaries - contents: - spm_aggregate_catch + - spm_aggregate - subtitle: Assembling and modifiying SSPM objects desc: Assemble the model object and prepare it for fitting - contents: - sspm + - sspm-class - spm_split - spm_lag - subtitle: Fitting the surplus production model desc: Fit the model - contents: - - fit + - spm + - sspm_fit-class + - sspm_formula-class - subtitle: Predict and plot desc: Predict and plot results - contents: - predict + - predict_intervals - plot + - plot_trajectory + +- title: Datasets + desc: Datasets used in the vignette +- contents: + - borealis_simulated + - catch_simulated + - predator_simulated + - sfa_boundaries + +- title: Accessors + desc: Accessors method for various objects +- contents: + - starts_with("spm_boundaries") + - starts_with("sspm_boundary") + - extract + - spm_data + - spm_unique_ID + - raw_formula + - method_func From 2c053149527a542b08c9f32e6793afed112f575f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 12:54:19 -0500 Subject: [PATCH 007/118] update old readme figures --- man/figures/README-unnamed-chunk-12-1.png | Bin 20262 -> 0 bytes man/figures/README-unnamed-chunk-21-1.png | Bin 43648 -> 0 bytes man/figures/README-unnamed-chunk-25-1.png | Bin 67256 -> 0 bytes man/figures/README-unnamed-chunk-25-2.png | Bin 68732 -> 0 bytes man/figures/README-unnamed-chunk-26-1.png | Bin 67869 -> 0 bytes man/figures/README-unnamed-chunk-26-2.png | Bin 72096 -> 0 bytes man/figures/README-unnamed-chunk-3-1.png | Bin 27205 -> 0 bytes man/figures/README-unnamed-chunk-9-1.png | Bin 58021 -> 0 bytes ...README-unnamed-chunk-8-1.png => patches.png} | Bin ...{README-unnamed-chunk-27-1.png => preds.png} | Bin ...ADME-unnamed-chunk-13-1.png => smoothed.png} | Bin 11 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 man/figures/README-unnamed-chunk-12-1.png delete mode 100644 man/figures/README-unnamed-chunk-21-1.png delete mode 100644 man/figures/README-unnamed-chunk-25-1.png delete mode 100644 man/figures/README-unnamed-chunk-25-2.png delete mode 100644 man/figures/README-unnamed-chunk-26-1.png delete mode 100644 man/figures/README-unnamed-chunk-26-2.png delete mode 100644 man/figures/README-unnamed-chunk-3-1.png delete mode 100644 man/figures/README-unnamed-chunk-9-1.png rename man/figures/{README-unnamed-chunk-8-1.png => patches.png} (100%) rename man/figures/{README-unnamed-chunk-27-1.png => preds.png} (100%) rename man/figures/{README-unnamed-chunk-13-1.png => smoothed.png} (100%) diff --git a/man/figures/README-unnamed-chunk-12-1.png b/man/figures/README-unnamed-chunk-12-1.png deleted file mode 100644 index 47e3ed5091ea525906f824f48c9db504668e6eea..0000000000000000000000000000000000000000 GIT binary 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zxFlWqdY4fFh^p0R?>G?1lL1~yX}_%;;}Y^f>hy#JJp%&+BcqhGG_DPybS?3Wb&wVT zUl{T8FOP(4q`twLAPAp8{jQ>6Z7pW~0L1jEzrJ91QCDZDC{yehsPB9g7Z;7$`49hz zi!ONT0no02Zq!5)lyr{>CI0H{osA{h!yWzh;Yjq_nrlWMdTNUGh|tx=rST$?U0pRZEo}^g;oVrn;Ycky zu{>Av-~2$XQS&qAG*QRi_pl9(xB0gWw>UXz0WmNz00BtfdJ9>UgoK2`@1yx7maMmL zdn(t!&@7^|DgTICLO%xvuD3L~tE)DHVT)`Z7#eEdJp*K=yE{9#WMu#2L9^_QBp*Pj znEft*qN}N@f`F5FGC>GwzTxwf&cpyRZ8P!Px7DsazZJ#)a&2u5$bGiSPQ}piudc1V zii*0d2f9e!P5x>v&~1Mf|sa4+2SgxxabQFXq2begB>{b1yF4 WmU6tFt5Gfjfu^pabGKB Date: Sun, 20 Nov 2022 12:59:19 -0500 Subject: [PATCH 008/118] improves readme, closes #55 --- README.Rmd | 95 +++++++++++++++++++++++++++++++++++++++++++++++------- README.md | 57 ++++++++++++++++++++++++++++++-- 2 files changed, 137 insertions(+), 15 deletions(-) diff --git a/README.Rmd b/README.Rmd index 10a99627..53d9eff6 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,5 +1,8 @@ --- output: github_document +editor_options: + markdown: + wrap: 72 --- @@ -14,28 +17,47 @@ knitr::opts_chunk$set( ``` # sspm - + + -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT/) + +[![License: +MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT/) [![R-CMD-check](https://github.com/pedersen-fisheries-lab/sspm/workflows/R-CMD-check/badge.svg)](https://github.com/pedersen-fisheries-lab/sspm/actions) -[![Codecov test coverage](https://codecov.io/gh/pedersen-fisheries-lab/sspm/branch/main/graph/badge.svg)](https://app.codecov.io/gh/pedersen-fisheries-lab/sspm) +[![Codecov test +coverage](https://codecov.io/gh/pedersen-fisheries-lab/sspm/branch/main/graph/badge.svg)](https://app.codecov.io/gh/pedersen-fisheries-lab/sspm) [![Downloads](https://cranlogs.r-pkg.org/badges/sspm?color=brightgreen)](https://CRAN.R-project.org/package=sspm/) -[![Latest Release](https://img.shields.io/github/v/release/pedersen-fisheries-lab/sspm?label=Latest%20Release)](https://github.com/pedersen-fisheries-lab/sspm/releases/latest) -[![CRAN Version](https://img.shields.io/cran/v/sspm?label=CRAN%20Version)](https://CRAN.R-project.org/package=sspm) -[![GitHub Version](https://img.shields.io/github/r-package/v/pedersen-fisheries-lab/sspm?label=GitHub%20Version)](https://github.com/pedersen-fisheries-lab/sspm/blob/main/DESCRIPTION) +[![Latest +Release](https://img.shields.io/github/v/release/pedersen-fisheries-lab/sspm?label=Latest%20Release)](https://github.com/pedersen-fisheries-lab/sspm/releases/latest) +[![CRAN +Version](https://img.shields.io/cran/v/sspm?label=CRAN%20Version)](https://CRAN.R-project.org/package=sspm) +[![GitHub +Version](https://img.shields.io/github/r-package/v/pedersen-fisheries-lab/sspm?label=GitHub%20Version)](https://github.com/pedersen-fisheries-lab/sspm/blob/main/DESCRIPTION) +[![DOI](https://joss.theoj.org/papers/10.21105/joss.04724/status.svg)](https://doi.org/) + -The goal of `sspm` is to implement a gam-based spatial surplus production model, aimed at modeling northern shrimp population in Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags based on Pedersen et al. (2020). However, it also aims to provide options for the user to customize their model. +The goal of `sspm` is to implement a gam-based spatial surplus +production model, aimed at modeling northern shrimp population in Canada +but potentially to any stock in any location. The package is opinionated +in its implementation of SPMs as it internally makes the choice to use +penalized spatial gams with time lags based on Pedersen et al. (2020). +However, it also aims to provide options for the user to customize their +model. ## Installation -You can install the released version of sspm from [CRAN](https://CRAN.R-project.org) with: +You can install the released version of sspm from +[CRAN](https://CRAN.R-project.org) with: + ``` r install.packages("sspm") ``` -You can install the development version from [GitHub](https://github.com/) with: +You can install the development version from +[GitHub](https://github.com/) with: + ``` r # install.packages("devtools") devtools::install_github("pedersen-fisheries-lab/sspm") @@ -43,10 +65,59 @@ devtools::install_github("pedersen-fisheries-lab/sspm") devtools::install_github("pedersen-fisheries-lab/sspm", build_vignettes = TRUE) ``` -## Vignettes +## Purpose + +The `sspm` package follows a strict workflow, where spatial boundaries +need to be defined and discretized into patches, so that we can apply a +spatial GAM to the data. You can either use of of our built in methods +for discretization or implement your own. Below is an example of a +discretized set of patches: + +![](man/figures/patches.png){style="display: block; margin: 1em auto;" +width="70%"} + +Then, we provide a framework to ingest trawling and fishing data, and +smoothing the model spatially: + +![](man/figures/smoothed.png){style="display: block; margin: 1em auto;" +width="70%"} + +Finally, the package allows for calculating the surplus production on a +yearly basis, and model the impact of different variables on that +productivity. You can then produce **one step ahead predictions** for +the biomass. -See the vignettes for an introduction to the `sspm` workflow. +![](man/figures/preds.png){style="display: block; margin: 1em auto;" +width="70%"} -```{r, eval=FALSE} +For an overview of the package design, please see our [workflow +diagram](https://pedersen-fisheries-lab.github.io/sspm/articles/Package_and_workflow_design.html). + +### Getting started + +See the example vignette for an introduction to the `sspm` workflow. + +```{r eval=FALSE} browseVignettes("sspm") ``` + +## See also + +`sspm` is opinionanted in its workflow and its use of GAMS, but other +frameworks exist to make use of surplus production models (usually not +in spatial capacity, however): + +- [`openmse`](https://openmse.com/features-assessment-models/3-sp/) +- [`TropFishR`](https://github.com/tokami/TropFishR) + +## Cite this package + +You can cite this package like this "we ran a spatial surplus production +model using the the R package sspm (Lucet & Pedersen 2022)". Here is the +full bibliographic reference to include in your reference list (don't +forget to update the 'last accessed' date): + +> Lucet, V., E. Pedersen (2022). The sspm R package: spatial surplus +> production models for the management of northern shrimp fisheries. The +> Journal of Open Source Software +> () diff --git a/README.md b/README.md index 91221ed4..9c7d888d 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ # sspm - + @@ -19,6 +19,8 @@ Release](https://img.shields.io/github/v/release/pedersen-fisheries-lab/sspm?lab Version](https://img.shields.io/cran/v/sspm?label=CRAN%20Version)](https://CRAN.R-project.org/package=sspm) [![GitHub Version](https://img.shields.io/github/r-package/v/pedersen-fisheries-lab/sspm?label=GitHub%20Version)](https://github.com/pedersen-fisheries-lab/sspm/blob/main/DESCRIPTION) +[![DOI](https://joss.theoj.org/papers/10.21105/joss.04724/status.svg)](https://doi.org/) + The goal of `sspm` is to implement a gam-based spatial surplus @@ -48,10 +50,59 @@ devtools::install_github("pedersen-fisheries-lab/sspm") devtools::install_github("pedersen-fisheries-lab/sspm", build_vignettes = TRUE) ``` -## Vignettes +## Purpose + +The `sspm` package follows a strict workflow, where spatial boundaries +need to be defined and discretized into patches, so that we can apply a +spatial GAM to the data. You can either use of of our built in methods +for discretization or implement your own. Below is an example of a +discretized set of patches: + + + +Then, we provide a framework to ingest trawling and fishing data, and +smoothing the model spatially: + + + +Finally, the package allows for calculating the surplus production on a +yearly basis, and model the impact of different variables on that +productivity. You can then produce **one step ahead predictions** for +the biomass. + + + +For an overview of the package design, please see our [workflow +diagram](https://pedersen-fisheries-lab.github.io/sspm/articles/Package_and_workflow_design.html). -See the vignettes for an introduction to the `sspm` workflow. +### Getting started + +See the example vignette for an introduction to the `sspm` workflow. ``` r browseVignettes("sspm") ``` + +## See also + +`sspm` is opinionanted in its workflow and its use of GAMS, but other +frameworks exist to make use of surplus production models (usually not +in spatial capacity, however): + +- [`openmse`](https://openmse.com/features-assessment-models/3-sp/) +- [`TropFishR`](https://github.com/tokami/TropFishR) + +## Cite this package + +You can cite this package like this “we ran a spatial surplus production +model using the the R package sspm (Lucet & Pedersen 2022)”. Here is the +full bibliographic reference to include in your reference list (don’t +forget to update the ‘last accessed’ date): + +> Lucet, V., E. Pedersen (2022). The sspm R package: spatial surplus +> production models for the management of northern shrimp fisheries. The +> Journal of Open Source Software +> () From 1361d36b44c1d42633c4089bce74e984f0fa93d7 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 13:07:12 -0500 Subject: [PATCH 009/118] re issue #55 --- README.Rmd | 7 +++++-- README.md | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/README.Rmd b/README.Rmd index 53d9eff6..6bf9c7b4 100644 --- a/README.Rmd +++ b/README.Rmd @@ -114,10 +114,13 @@ in spatial capacity, however): You can cite this package like this "we ran a spatial surplus production model using the the R package sspm (Lucet & Pedersen 2022)". Here is the -full bibliographic reference to include in your reference list (don't -forget to update the 'last accessed' date): +full bibliographic reference to include in your reference list +(publication pending, don't forget to update the 'last accessed' date): > Lucet, V., E. Pedersen (2022). The sspm R package: spatial surplus > production models for the management of northern shrimp fisheries. The > Journal of Open Source Software > () + +Or see the [package citation +page](https://pedersen-fisheries-lab.github.io/sspm/authors.html#citation). diff --git a/README.md b/README.md index 9c7d888d..f7d434e3 100644 --- a/README.md +++ b/README.md @@ -99,10 +99,13 @@ in spatial capacity, however): You can cite this package like this “we ran a spatial surplus production model using the the R package sspm (Lucet & Pedersen 2022)”. Here is the -full bibliographic reference to include in your reference list (don’t -forget to update the ‘last accessed’ date): +full bibliographic reference to include in your reference list +(publication pending, don’t forget to update the ‘last accessed’ date): > Lucet, V., E. Pedersen (2022). The sspm R package: spatial surplus > production models for the management of northern shrimp fisheries. The > Journal of Open Source Software > () + +Or see the [package citation +page](https://pedersen-fisheries-lab.github.io/sspm/authors.html#citation). From a0f61cbd07f249a53663533295578aa74d5c0ffd Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 20 Nov 2022 13:10:40 -0500 Subject: [PATCH 010/118] closes #53 --- vignettes/Package_and_workflow_design.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/Package_and_workflow_design.Rmd b/vignettes/Package_and_workflow_design.Rmd index b98a916e..1b9fcb65 100644 --- a/vignettes/Package_and_workflow_design.Rmd +++ b/vignettes/Package_and_workflow_design.Rmd @@ -24,7 +24,7 @@ The package follows an object oriented design, making use of the S4 class system 3. The second pillar is the recognition of 3 types of data: **trawl**, **predictors**, and **catch** (i.e. harvest). The next step in the workflow is to ingest the data into `sspm_dataset` objects via a call to `spm_as_dataset()`. -4. The first proper modelling step is to smooth the biomass and predictors data by combining a `sspm_dataset`, and a `sspm_discrete_boundary`. The user specifies a gam formula with custom smooth terms (see \autoref{tab:formula} for more details). The output is still a `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. +4. The first proper modelling step is to smooth the biomass and predictors data by combining a `sspm_dataset`, and a `sspm_discrete_boundary`. The user specifies a gam formula with custom smooth terms (see the [details section of the `spm_smooth()` function](https://pedersen-fisheries-lab.github.io/sspm/reference/spm_smooth.html#details) for more details). The output is still a `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. 5. Then, catch is integrated into the biomass data by calling `spm_aggregate_catch` on the two `sspm_dataset` that contains catch and smoothed biomass. Productivity and (both log and non log) is calculated at this step. From 88586677e71630f8f03024d4899f498f8ab51e32 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:31:09 -0500 Subject: [PATCH 011/118] closes #18 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index dbd6a006..a08cd328 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,7 +37,7 @@ affiliations: -Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the environmental conditions governing parameters affecting that rate of change (such as the maximum rate of growth or the carrying capacity of the population) stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. +Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change (such as the maximum rate of growth or the carrying capacity of the population) stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. From c871189a1b197bf6de1c83acf2d195d2e78e30db Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:31:09 -0500 Subject: [PATCH 012/118] closes #63 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index dbd6a006..a08cd328 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,7 +37,7 @@ affiliations: -Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the environmental conditions governing parameters affecting that rate of change (such as the maximum rate of growth or the carrying capacity of the population) stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. +Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change (such as the maximum rate of growth or the carrying capacity of the population) stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. 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Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change (such as the maximum rate of growth or the carrying capacity of the population) stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. +Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. 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Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations, and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. +Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. From 0dee93a6ac39f9ce24d69b77777f3a2796abfa22 Mon Sep 17 00:00:00 2001 From: VLucet Date: Sat, 26 Nov 2022 21:40:21 +0000 Subject: [PATCH 017/118] [Bot] build pdf --- paper/paper.pdf | Bin 522017 -> 522017 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 93464a29908c76ce25cd0291255f7dcd71e41eda..3f5ecc53c07a0ad50f07ce6b477f357194feed7a 100644 GIT binary patch delta 3008 zcmaitWmFRk1BEH+h9M)QVQMg8(i-(6O#5B-h{Nf3W zStTq=yLE%x_{+-##SiA2Zt{5n4E@IE z;O+EBtN1k4dLmt&PcC0hi?tV_9-8WMER-lO_s{&3xMVM%k%R~80@+1|*h+bgt?|C` zDLe&+r{p_ZjK@#SiWC#2WUS>ux03+WDBBT%PeNsmzf=p6`3p6vrf+q6pE5+Nz)JSm z`QPa@TqzXTecA1SB=j*7%6S@Gl?c_M<1cI6z4^k%cP3Cae!QKAZ=XgdugXGWyY1#o znpmvIW0Zd0IT!XFSf>jlO@Y7WIKW~bm)1;MBkv!3R9N<}?-mnt5W>rs<~sI%$*6(p z-ff?xaoH#_H|^v|zzI;k3L`w4_JFi8CU5R=5Mi%>pae8w&PhbNS0`V1cHctx`ltDz z3$Qv6q=o7le7;&M`up98Ro00LPMUL@Fs|?<<_>tVKZqgF-)2*3(CM#R+H7YDCvG2V zXej^YRj~m@=4K-LvG=4mE~2^kdO4ftq#ibUmg>>uC zk+5%@Ns_!q;wD{hN!CMA2R+$6Ls2nG9iAbbe=IQHaY2yfa{!&)Kdhu$+|Mnh+G&*{fkP*}-eQYr#8}2K1VOXiLjbahgpMVb@#` zNnE=}emNq?af38kL1buQX~|^#JYIK+p+>{re|T*c4e-=bzoHheWfh7`S8_m&AP;kS zS(4LSpZKL9??G|Tm*giau(3qFmro!-xk4k$x7AN~aGsVUWHcGcMipRYA@-A^+X(6U z3woKwboz*{WVpGRw?afgh+dzKehF5d(D^r&G|S^bcz~wYZ5gfwz+Ylzm^^{M$o#mC z#5~B;WN`p-5i^iYOK=F;f^^WHuSb&V9PyH$M6+t)8XmV}m3{}dne1Io06s zv2Q{+c6PY^DxCK`$DFB&!8JdQpCkI&l`?WB7n!_?6+;S^xlWj!A-`o=aGh*}r{c1$ zZTZ2*6_*qTCiKSgrIlaG2(P zlUr$Bs`N+$c8Je(e~-(4bbM5#XO$XNv{$xNl+zO)Shwr*5Ost6TY_F9(tW(8FHv7Ptx*0o`iZ;6tKWlI4>?Ad@Ma;Ub;^Y_L z@zqXnH3^>jx31mFx$(}BbV4vTU={E#7)S;SdG#-})D+Iz7k^`eE>_cefA3VUL$FIKjSi})N2V6y&gY+$L3vF z8NaAVxa;(_O2iZ2Kq*N`ufH6uLf_$_BNC7yM%}rZ-|eMQVH|(~tYF@fYq=lfs)STG z9@EpopH;lp?}CQRcsg}u{h0i#T22;~w6c`#G8=KH&p;G@R;J#|tS@nex3c%U&=uJ3Q$Zqe!&~D*hsw99J&! 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Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. -Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. +Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters, such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. From a22944f09a403297ce034e46dc590c8f4ed05d75 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:45:34 -0500 Subject: [PATCH 019/118] closes #68 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index f27c40da..88ec0572 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -39,7 +39,7 @@ affiliations: Population modelling is an exercise of interest within environmental sciences and adjacent fields. Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time [@gotelli_primer_2008]. Modern population models increasingly acknowledge the non-stationary nature of wild populations and work to incorporate environmental fluctuations into dynamic models [@thorson_importance_2015; @thorson_using_2017]. Population models designed to answer applied resource management questions, such as fisheries stock models, increasingly address how the dynamics of stocks vary across space and time. -Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters, such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. +Resource managers are becoming increasingly interested in how variation in ecosystem factors such as predator abundance and abiotic variables impact the spatiotemporal variability of population parameters, such as productivity [@zhang_longer_2021; @szuwalski_climate_2016]. Further, treating spatially structured stocks as single unstructured stocks can lead to substantially biased estimates of population change [@thorson_importance_2015]. However, stock models that explicitly incorporate spatial dynamics and time-varying ecosystem variables are still rare in fisheries science, despite the push for more ecosystem-based management methods in fisheries management [@crowder_impacts_2008; @berkes_implementing_2012; @tam_towards_2017]. From 3d68c40ac1adf709c50649aa20b1a2f224b6ff3a Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:46:26 -0500 Subject: [PATCH 020/118] closes #69 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 88ec0572..4af0ffe7 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries, and are based on modelling changes in the total biomass of a stock in a given location over time (i.e. *surplus production*) as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age and sex structure of the population is not accessible, or when age- or length-structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time (i.e. *surplus production*) as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age and sex structure of the population is not accessible, or when age- or length-structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. From 6312dc5cd3e2be997b123aa54acc53ca2e7c6d61 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:52:26 -0500 Subject: [PATCH 021/118] closes #70 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 4af0ffe7..8303ceb1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time (i.e. *surplus production*) as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age and sex structure of the population is not accessible, or when age- or length-structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age and sex structure of the population is not accessible, or when age- or length-structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. From f1e7beb45004d3a633f8819792209b2f535ca14d Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:53:49 -0500 Subject: [PATCH 022/118] closes #71 closes #72 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 8303ceb1..c1c1a2e8 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age and sex structure of the population is not accessible, or when age- or length-structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. From b276172a2072d3f4bfe66ad2414486107b34ccb7 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 16:56:24 -0500 Subject: [PATCH 023/118] closes #73 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index c1c1a2e8..430eace1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Classically, SPMs assumed single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. From c2839ed32e5ce310b0be9ce2cc95c1feffedb00e Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:05:19 -0500 Subject: [PATCH 024/118] closes #74 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 430eace1..23b8a9fd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008], and as such have been viewed as a limited tool for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. 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Subject: [PATCH 111/118] addressing issue #125 --- paper/paper.bib | 24 ++++++++++++++++++++++-- paper/paper.md | 12 +++++++----- 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index ba5c1b5c..ab1cea92 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,3 +1,22 @@ +@article{thorson_spatio-temporal_2019, + title = {Spatio-temporal models of intermediate complexity for ecosystem assessments: {A} new tool for spatial fisheries management}, + volume = {20}, + copyright = {© 2019 John Wiley \& Sons Ltd}, + issn = {1467-2979}, + shorttitle = {Spatio-temporal models of intermediate complexity for ecosystem assessments}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12398}, + doi = {https://doi.org/10.1111/faf.12398}, + language = {en}, + number = {6}, + urldate = {2021-03-30}, + journal = {Fish and Fisheries}, + author = {Thorson, James T. and Adams, Grant and Holsman, Kirstin}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/faf.12398}, + pages = {1083--1099}, +} + + @book{rueGaussianMarkovRandom2005, title = {Gaussian {{Markov}} random fields: theory and applications}, author = {Rue, Havard and Held, Leonhard}, @@ -298,10 +317,11 @@ @techreport{dfo_assessment_2019 @techreport{pedersenNewSpatialEcosystembased2021, type = {Can. {Sci}. {Advis}. {Sec}. {Res}. {Doc}.}, title = {A new spatial ecosystem-based surplus production model for {SFA} 4-6 {Northern} {Shrimp}}, - number = {2022/nn}, + number = {2022/062}, author = {Pedersen, Eric J. and Skanes, Katherine and Le Corre, Nicolas and Koen-Alonso, Mariano and Baker, Krista}, year = {2022}, - pages = {in press}, + pages = {1-64}, + url = {https://publications.gc.ca/collections/collection_2022/mpo-dfo/fs70-4/Fs70-4-2022-012-eng.pdf}, } diff --git a/paper/paper.md b/paper/paper.md index 2bd44fa1..d8c872c1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -81,17 +81,19 @@ The key workflow steps are: ![The sspm workflow.\label{fig:workflow}](../man/figures/flowchart.png){width=90%} -# Connections to other spatiotemporal stock assessment approaches +# Connections to other surplus-production based stock assessment approaches -The general approach used by `sspm` of using a statistical model to estimate spatiotemporally varying population varying biomass indices is closely connected to approaches used by other modern model-based spatial abundance estimation software, such as the `VAST` R package [@thorson2019Guidance] and the `sdmTMB` R package [@anderson2022sdmTMB]. Our method shares the same approach as both `VAST` and `sdmTMB` of using spatially explicit models to estimate local biomass density (\autoref{fig:workflow} steps 1,2, and 4), then aggregating up from those models to predict aggregate stock-level metrics such as total biomass and productivity (\autoref{fig:workflow} steps 8). The multiplicative surplus production model used by `sspm` is also similar to the vector-autoregessive model for biomass changes used by these two packages, as both `VAST` and `sdmTMB` can model local temporal changes as autoregressive processes on the link-scale of a generalized linear model [@thorson2019Guidance]. +The `sspm` package uses a model-based, random-effects based approach to estimate the effects of ecosystem drivers on surplus production across space and time. Our approach is conceptually related to the stochastic stock assessment approaches used by the R packages `spict` [@pedersen_stochastic_2017] and `jabba` [@winker_jabba_2018] R packages for surplus production modelling, in that we assume that biomass dynamics can be modelled as effectively a logistic growth model with both process and measurement error. While `sspm` does not currently have the capacity to model biomass dynamics as a continuous-time process, as with `spict`, or incorporate prior parameter information on catchability or biomass dynamics as in `jabba`, `sspm` can model spatially and temporally varying productivity, which is currently not possible in these models. -One major difference between the `sspm` package and other model-based spatiotemporal modelling packages is its special-purpose nature. The `spm_smooth` function uses a computationally simpler (although somewhat less flexible) Conditional Autoregressive (CAR) model [@rueGaussianMarkovRandom2005] for modelling spatial variation in covariates and biomass, as compared to the more complex spatial random effects possible with `VAST` and `sdmTMB`; this has the advantage of computational speed and less user knowledge of how to set up complex spatial grids, although it is less flexible. This means that `sspm` should be easier to adapt to novel fisheries than more complex packages that require more user modelling knowledge. +The `sspm` package can be viewed as a spatiotemporal Model of Intermediate Complexity [a 'MICE-in-space' model; @thorson_spatio-temporal_2019] that can incorporate effects of other species and ecosystem drivers as well as changes in fishing pressure on stock status. Our approach is closely connected to approaches used by other modern model-based spatial abundance estimation software, such as the `VAST` R package [@thorson_spatio-temporal_2019] and the `sdmTMB` R package [@anderson2022sdmTMB]. Our method shares the same approach as both `VAST` and `sdmTMB` of using spatially explicit models to estimate local biomass density (\autoref{fig:workflow} steps 1,2, and 4), then aggregating up from those models to predict aggregate stock-level metrics such as total biomass and productivity (\autoref{fig:workflow} steps 8). The multiplicative surplus production model used by `sspm` is also conceptually similar to the vector-autoregessive model for biomass changes used by these two packages, as both `VAST` and `sdmTMB` can model local temporal changes as autoregressive processes on the link-scale of a generalized linear model. The `sspm` package cannot, however, model the dynamics of multiple species simultaneously; multi-species modelling would require generating a separate surplus production model (\autoref{fig:workflow} steps 5 and 6) for each species of interest. -The other benefit of `sspm`, relative to other modelling packages, is the ability to model productivity rates directly (\autoref{fig:workflow} steps 5 and 6), rather than implicitly via an auto-regressive processes as used in `VAST` or `sdmTMB`. This means that it is possible in `sspm` to model nonlinear relationships between environmental covariates and productivity, or to easily include factors such as time-lagged effects of predictors on productivity in a given year. This approach does, however, sacrifice the ability to propagate measurement error into uncertainty about rates of change. One of the future directions for development of this package is to include variance propagation methods into the surplus production modelling step. +One major difference between the `sspm` package and other model-based spatiotemporal modelling packages is its special-purpose nature. The default `spatial_smooth` function uses a computationally simpler (although somewhat less flexible) Intrinsic Conditional Autoregressive (ICAR) model [@rueGaussianMarkovRandom2005] for modelling spatial variation in covariates and biomass, as compared to the more complex spatial random effects possible with `VAST` and `sdmTMB`. This has the advantage of computational speed and less user knowledge of how to set up complex spatial grids, although it is less flexible. This means that `sspm` should be easier to adapt to novel fisheries than more complex packages that require more user modelling knowledge. Further, it is possible to specify alternative spatial smoothers than the ICAR model in the `spatial_smooth` function via the `bs=` argument, although this functionality has not been well-tested and should be considered experimental. + +The other benefit of `sspm`, relative to other modelling packages, is the ability to model productivity rates directly (\autoref{fig:workflow} steps 5 and 6), rather than implicitly via an auto-regressive processes as used in `VAST` or `sdmTMB`. This means that it is possible in `sspm` to model nonlinear relationships between environmental covariates and productivity, or to easily include factors such as time-lagged effects of predictors on productivity in a given year. This approach does, however, sacrifice the ability to propagate measurement error into uncertainty about rates of change. One of the future directions for development of this package is to include variance propagation methods into the surplus production modelling step. # Acknowledgements -This research was supported by the Canadian Department of Fisheries of Oceans (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the Canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. +This research was supported by the Canadian Department of Fisheries of Oceans (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the Canadian Natural Sciences and Engineering Research Council (NSERC) to E.J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. # References From c9dfa2607c87b2de3e6ec8d2385417f65ad38096 Mon Sep 17 00:00:00 2001 From: eric-pedersen Date: Tue, 2 May 2023 18:35:54 +0000 Subject: [PATCH 112/118] [Bot] build pdf --- paper/paper.pdf | Bin 520820 -> 522870 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 7214dfb2b01988d7447bef1c8e590c1b65ea0e0c..b17d47083d0065750362c39a15ef0eedb34a70c0 100644 GIT binary patch delta 45139 zcmV)WK(4>^RG!Vp_ibwad`gkLA}lDYcW*82Ht21^C;&|MvCs#>OsRM|=BaW5&L4 zUX?XM`?xq4h1ao3C#7U<8N&W7uaxuU#w8IvKK|*iKh$#m1J-_Qb>*W_3b?RR3U5M3nFv!trD>bU*ja4NPmAcLV1^ZN7x`*zgUriRBrG9;1yC_ zNmc1~`F(n$s%ACmWJPp}P<3rW9E8@MD<*#Rf8++H^=JcOmtMAt1XiJe+lsa5*(Myk z)&ZPZa2uw9h84iDWQ^d7I8V_5qx0;~bGu-xABvgg4ij2NaBOxaQ&gT&VjJM|-x2LK z+rIPFUTyisrv+!GK)zaN1FUPp2hQXLA=wG!_wr1n+gp2Wj;Gm1nd!m!KijPAE*vgj ze->TQ?TjIoyCW%dag&Kwcg;TQytd8!G}~r@mu~kS*L8zCYxB1Fd=hq}atL|S4L!o zJ!I99C(aWJxU7zbl)e7tS(FSKC6J`=fW!vjefJ8e2HoOK*4`;^&!O2iBkv}HqV?Rg z=#Gr0KCyjF)w^v&^kjv0b4tW1pZJ<4T4u_&D>e(?Io!c*RWg59_v|NUQ&%-@e}0$E zu-5`4LtEJOwd68(gMiafSQ%zTVFXC9%?RYq=2=XQbO}_aCqZQ)iPpx6KrvJ{ddYs?A|6LBF7S9%Ig9I#lt~@&rnRFuziDLfO7- zxi36d=O;v=lM-;2nMDC`@ivZLf9X*DMgtvdCORhoT7WG?Rj=T;^8m(>v2aK*`cKx4 zaYoqaCaT32Lc@A`CpBgDWC>^NmyAvJJa#4d_@v~mwQ%@}yOJ+vnc{7;Y@_l<`r#x$ z4aZ^bNjn_aEs`)<5|8Aavv1c%1ZR83*K%gVo#|oRbw_DlxlRqnv88vFf9MGb3{gOH zPmJfj>M!l&$qP#t?oGF;Si@Mpa2hni)$FElsrwPkj-xD^B=hiX%lNt*t#^HIwuyk1(-8 z_Jj~Q_VpHGMs#VG{yhgirbDM3IQq2?hZM@AG>RgwD9&dgI4(sHG>%k1YMA$#TMP~Cu7$|^i&i=G>VBB^C!wGfIC7F1Se@p7;**-^ew((R! 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+++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/paper/paper.bib b/paper/paper.bib index ab1cea92..ee274aad 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,3 +1,36 @@ + +@article{pedersen_stochastic_2017, + title = {A stochastic surplus production model in continuous time}, + volume = {18}, + issn = {1467-2979}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12174}, + doi = {10.1111/faf.12174}, + language = {en}, + number = {2}, + urldate = {2023-05-02}, + journal = {Fish and Fisheries}, + author = {Pedersen, Martin W and Berg, Casper W}, + year = {2017}, + pages = {226--243}, +} + +@article{winker_jabba_2018, + title = {{JABBA}: {Just} {Another} {Bayesian} {Biomass} {Assessment}}, + volume = {204}, + issn = {0165-7836}, + shorttitle = {{JABBA}}, + url = {https://www.sciencedirect.com/science/article/pii/S0165783618300845}, + doi = {10.1016/j.fishres.2018.03.010}, + language = {en}, + urldate = {2023-05-02}, + journal = {Fisheries Research}, + author = {Winker, Henning and Carvalho, Felipe and Kapur, Maia}, + month = aug, + year = {2018}, + pages = {275--288}, +} + + @article{thorson_spatio-temporal_2019, title = {Spatio-temporal models of intermediate complexity for ecosystem assessments: {A} new tool for spatial fisheries management}, volume = {20}, From 17d5f1615c477315614b303ddf9c59e5221077f5 Mon Sep 17 00:00:00 2001 From: eric-pedersen Date: Tue, 2 May 2023 18:38:20 +0000 Subject: [PATCH 114/118] [Bot] build pdf --- paper/paper.pdf | Bin 522870 -> 523697 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index b17d47083d0065750362c39a15ef0eedb34a70c0..778d95561587f9a517449a6b2fe55190f53277d2 100644 GIT binary patch delta 25781 zcmZs?Q>-pb(=@!7ZQHhO+qP}N!QW@k^&51W`2zx_Wgd(eTDD-9Ow7mpGS`WB5@djsR-|uAWnmN zeYOeoe$R27v>)!P|N4#@sPt$?u>frH|1rKnXZ`MM{K9j;-Cu4x7zl4<&*ypV4_&VU zY~jyV3EKL0xzfX2N)HUM1n7TueZLM@chOONem1f@}>v0Z~S~y 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12 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index a8ffe11e..177ec985 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -125,7 +125,8 @@ @article{karp_accounting_2019 editor = {Maravelias, Christos}, month = apr, year = {2019}, - pages = {fsz048}, + pages = {1305--1315}, + volume = {76}, file = {Karp et al. - 2019 - Accounting for shifting distributions and changing.pdf:/home/vlucet/Zotero/storage/JP6BDALA/Karp et al. - 2019 - Accounting for shifting distributions and changing.pdf:application/pdf}, } @@ -242,10 +243,12 @@ @article{zhang_longer_2021 @article{prager_suite_1994, title = {A suite of extensions to a nonequilibrium surplus-production}, + journal = {Fishery Bulletin - National Oceanic and Atmospheric Administration}, language = {en}, author = {Prager, Michael H}, year = {1994}, - pages = {17}, + volume = {92}, + pages = {374--389}, file = {Prager - A suite of extensions to a nonequilibrium surplus-.pdf:/home/vlucet/Zotero/storage/4HHXRZ4Y/Prager - A suite of extensions to a nonequilibrium surplus-.pdf:application/pdf}, } @@ -297,7 +300,6 @@ @book{gotelli_primer_2008 file = {Snapshot:/home/vlucet/Zotero/storage/L24AL4DI/a-primer-of-ecology-9780878933181.html:text/html}, } - @article{thorson_importance_2015, title = {The importance of spatial models for estimating the strength of density dependence}, volume = {96}, @@ -313,7 +315,6 @@ @article{thorson_importance_2015 pages = {1202--1212}, } - @book{walters_fisheries_2004, address = {Princeton, N.J., U.S.A.}, title = {Fisheries ecology and management}, @@ -340,7 +341,7 @@ @book{wood_generalized_2017 @techreport{dfo_assessment_2019, type = {techreport}, institution = {Canadian Science Advisory Secretariat (CSAS)}, - title = {An assessment of {Northern} {Shrimp} (\textit{{Pandalus} borealis}) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} (\textit{{Pandalus} montagui}) in shrimp fishing area 4 in 2018}, + title = {An Assessment of {Northern} {Shrimp} (Pandalus borealis) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} (Pandalus montagui) in Shrimp Fishing Area 4 in 2018}, number = {2019/027}, author = {{DFO}}, year = {2019}, @@ -348,17 +349,13 @@ @techreport{dfo_assessment_2019 } @techreport{pedersenNewSpatialEcosystembased2021, - type = {Can. {Sci}. {Advis}. {Sec}. {Res}. {Doc}.}, - title = {A new spatial ecosystem-based surplus production model for {SFA} 4-6 {Northern} {Shrimp}}, - number = {2022/062}, + type = {Canadian Science Advisory Secretariat (CSAS) Research Document}, + title = {A New Spatial Ecosystem-Based Surplus Production Model for {SFA} 4-6 {Northern} {Shrimp}}, author = {Pedersen, Eric J. and Skanes, Katherine and Le Corre, Nicolas and Koen-Alonso, Mariano and Baker, Krista}, year = {2022}, - pages = {1-64}, - url = {https://publications.gc.ca/collections/collection_2022/mpo-dfo/fs70-4/Fs70-4-2022-012-eng.pdf}, + url = {https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html} } - - 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zGIo8Pnu0aCJf>2>Po#H+>`EJ9-e3XW=I8=1d8Zz>7Vz~F#piwb_}Fj#l$W#=mp9R2 z`sQn7oSt;d%jL%m4x)}`=kwm(J083REYaZ)dI~pt$P+-flWeh+CMrF#qEVkf-#0^n e8CbuN&QOD$p28z80Cd=V{6K7GW;qRc?EeP|BUAnW From d3bd122fe35d35edb917a61b6c7a5426a0c8e996 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:19:16 -0500 Subject: [PATCH 026/118] closes #75 closes #76 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 23b8a9fd..bf65b9e2 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,7 +45,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. -Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure. One example is that of the Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime [@dfo_assessment_2019]. These stocks currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. +Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure.The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. From 9a1a6ebf3bb4051c01ff115576314eab1abdeef7 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:21:06 -0500 Subject: [PATCH 027/118] closes #77 closes #78 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index bf65b9e2..1f7a293f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,7 +45,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. -Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure.The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change, and to predict how fishing pressure and changing environmental conditions may affect future abundance in the region. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. +Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure.The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change and to predict how fishing pressure and changing environmental conditions may affect future abundance. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. 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z{hF4s%*QhKf+#Sr*KK%GCY95qZH(MYb&1Yxb%Xb(swKlgZJ)_Nz~H{xfeyls?~$_+ zvMRDF$fi{B6tV8}VppCG%?F|b3vpEINg6a4h4MN@1y37WIGr3VGN5Exgx~!_N0+DFXge2 zxH)_8s)y)z9#xM;{c0XwPzHl>UfXgU_cQ=0lSA9F3V<3WP%&+#U#BTzC|frne_&w=`hhb=|Bk&OUslp}28nZdbAhxh(K~Oh{t<3`yH_|3^Mx@bOCYg2 zrHfb1!)Bk~lU#!QRn5aF9@hqi`EW$?5z~@(Y#u(iJ3IxOnO;-u3#u+kMLx!ygh*u= zdM&oTafJMi!;?3BWuU$;ydABh-=@>cd=y>@F8ff{TKv>{^w26agBHghOvwi7Un>F_ z<)qmRPi2;j*&8FX?hf^Yw({UTyH&p-wwYkM_j>Zf0M@bAeuf_5?kU{*hOIM@?o?Ub z({kQ{CuDy$jLRvnC-*Y)XxWy`C`Y}oW;E1lEpb=bi=v}Zd^klc%aO&=rfq2_DYW=D zn8T}|OLDXm{pKtG`QmP~P1ZY;H-p_yg(od8oNLX)OOHYWO}GOO^aGcan?2}e?@Q&N z(NM#I&GUgaB*9mf(KE!kQv-wq4|h{HIvAG&++LnP4;*hWzmz)}|8Aw+++8h99R6*p c+}urE-MybbDN%VqoSYyoR9adYHCfdE0Vq~(IsgCw From b0f3814a6b40864401a25f6e1c8eef1b60b3f9a6 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:23:07 -0500 Subject: [PATCH 029/118] closes #79 closes #80 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 1f7a293f..904d389b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -49,7 +49,7 @@ Two limitations of classic SPMs is the assumption of spatially constant producti -Population models like SPMs usually fall under two categories: process-based and statistical. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions, and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via a R package `sspm`. +Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions, and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via a R package `sspm`. While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], The R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. From d9a29a6672bc33c65fb7cb807e08db0ddc91cb8e Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:24:02 -0500 Subject: [PATCH 030/118] closes #81 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 904d389b..232504fe 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -49,7 +49,7 @@ Two limitations of classic SPMs is the assumption of spatially constant producti -Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions, and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via a R package `sspm`. +Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via a R package `sspm`. While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], The R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 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Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via a R package `sspm`. +Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], The R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. From 3476df78ea37f553fe807264402656ae620ece79 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:26:07 -0500 Subject: [PATCH 033/118] closes #83 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index d18e25e5..37d3488b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -51,7 +51,7 @@ Two limitations of classic SPMs is the assumption of spatially constant producti Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], The R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. +While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation or Delaunay triangulation). 2. 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z=fyxOqP@E2u~aUWOtPXEH$Es7ObAP%xX|X}Y7cia%`_x68n)=jp2w~SuLCy3<^epN z!o89e*eMjWA}bMtF+){byJcNXId)%K=P@je>Ta28dHLo16ce#)2W_%KOT?1N7fdTu z^N(InJnlgMHy!u^5!#Iss-Tz z{}DXh91VoXEX>aLx|I7S`LW1yd3$#&06kbtL z;n)HzG(i5 zC<2xf5fv7cqL=vpmzbqv(;@K*Wqq$kCk0rhP28c{B!lOjK>-_l36-TJHRDaQ_olv; zHmWAavyLAMM%0Prlv0UAlw>QeQNLi;%6&@U^kia8yF+rLjwDj%Ow*$jDV+z}?(KGS z0&mxyk4YgIHbjO#=%2|?&@unBXIu4#mCrhu+cO1<2f%PYcDhV$6+lL-D1$!UcqcU7G7j~$eXc9e)iZlEyOoU zeJVyQB~L&`M!g!uYnJl7ZKjbUo9+BvjvYK!G7lfyl7TC7pdQ7(k2?OX&l0(X`6D?h z<9@f#tw;xpu9GfLt*Pl7h39d$_rSeHg?eY{Ts@hS6-{+4Zatx}49!vG4&#!PbcjH8 z1ZBL5d0-1_ORw-Eo_H}Wr6p?Yj7!?R1>vL*gebZ9+Y?jM3CS#*cHYCCxzwvg^V50d0QOfdWXXZ)J)(i1@oAVAXY3KpS@ zra>WKHwNRIL9(2Oz1J;N^Jd|A_)Enbl*^_)7w4MG!I!%Z+OM8kb!cj#%Awp|RsDRN z>-m!A$%lCg&BKSP$_{deb From b4dba56ca03e410232d3e4b6da116fdf035fe5a3 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:27:23 -0500 Subject: [PATCH 035/118] closes #84 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 37d3488b..95b9c1aa 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -51,7 +51,7 @@ Two limitations of classic SPMs is the assumption of spatially constant producti Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. +While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation or Delaunay triangulation). 2. Spatiotemporal smoothing of biomass and environmental predictors using GAMs. From ced975177fc42956a13132b1cbcfb927816593ba Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:32:41 -0500 Subject: [PATCH 036/118] closes #86 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 95b9c1aa..bf2efe22 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -53,7 +53,7 @@ Population models like SPMs usually fall under the two following categories: pro While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. -1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation or Delaunay triangulation). +1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). 2. Spatiotemporal smoothing of biomass and environmental predictors using GAMs. 3. Computation of surplus productivity based on biomass density and fishing effort. 4. Fitting of SSPMs to productivity data with GAMs. From 6d0bd94329efbc0a146d7f64356e6891cfa3d2b2 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:33:28 -0500 Subject: [PATCH 037/118] closes #87 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index bf2efe22..1290ff82 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -60,7 +60,7 @@ While the initial application of this model was modelling Newfoundland and Labra 5. Visualization of results, including confidence and prediction intervals. 6. One-step-ahead prediction of biomass for model validation and scenario-based forecasting. -Although it was developed in a fisheries context, the package is suitable to model spatially-structured population dynamics in general. +Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general. # Package design From 1456db8e513205351d9dfff805f7a62c3e58ff02 Mon Sep 17 00:00:00 2001 From: VLucet Date: Sat, 26 Nov 2022 22:34:28 +0000 Subject: [PATCH 038/118] [Bot] build pdf --- paper/paper.pdf | Bin 521864 -> 521890 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index f72b45f5b5b1319506d8303e3fd6b984c981d8ea..7479e9f09a45460ae7090dea4428273aa717ef01 100644 GIT binary patch delta 6242 zcmaiyRa6uV*sVWmK)M@gq+w_zgdwGdl#U^W4jH+i)HRY&bv78d2b77*??=S!Ko0EWX=^bcPr>DL*{EzD_ugAwIlZIZ}KZ zev;lSN(L#zv@|iP77#yP?rz{)p{7rS&ew?8mySi{Vp0MpM3Yd=i`>MIrkgEw^euP* z{1Ktd?wiaKK_}Ds{FFO^^!&7Qvvg~v;i2ECcFNx6_u%m1<>rznIZ`6vWCC&fF%~i4 zoh*;F*E_LFmyPM}ZXPqZ%W!D%qGXWvTBqT)^!D}U;KMHBlD7PG%kvS_9C&iG|I9#a zT*_LYHCZ**7(p&RJ{6hp*7tj8S~FJ){G+nfEZ@pQxA?5}{dtM`!z}k78CKfSju)@F z)tc+^2Z1j5LOXZY(8Wq-lQuW)tZ-dG<&x*!V6*dTa+4>~Kf$xN^DQrNZvq=iHY_eC z-XWSVV+^D@>|wb!N4G*W^BKRk#wwyO#@z5%p4{(58QqDu532T+?%!1t$!Zk*$+wJJ$zmz)Bd24+9m|-|W z+vY5beLl=c(PsOYFJ=tq#rQAmO#yv=j0Yd4ezXZLpwPvnwM=-+^Uv(KSX+m!9>hMGM}f zTIqte{A*Er4kxD2G_V#;8+zy*f-R*V8P6_pg;===`zV)HQ-R(Uv}geMS@TJAX%i_` zXM>HgjA64~6$-b{s%iu=(+4{ySBIQ7*6?c@Hg7X@+kBufg`%5=UsnD+w{pzH{ZHsT zsCvh{@re$w4!P0_z(5QYZhkcxdp+IM}J{g|)h!=rJG{Bs8R%XfgE6h`P zfMjlVGLP#|OLkKQT0QCsj1kLt#a%O!86|V04mQS8OpXn zoOG=fU-0RS)MY{C*EDRGeh_giYh-E2kgHcK336&cqU0$k5nSWMY%sCke&0ihk>mA% zW!RJiS?Cs1QYNjM)||mdbh=*(N%1?Ag%EjZeQ>qvef_;L&QHff?!z-4{JT#VJje%P^ z=y0ct9ucj4Q(oq1M>hVqBWwmiom7@+OCq@?jrEO4&pmZ4T4l$T2Cd3{MfE)bR3lLW?^EJd6KCpv~`>6hO3EVee`X9zAfA(N^R)iTJK zT~Gv+vn$^6qcD8~a(yPWw3rnX*zQ{rq*Xb*N$>__84?fw_6X<}+7R2hub(1z@8T#| zjq$(9`85L`$5}OB`Iau6x$Bfm$%Nn4;Q&wV0Fbt~P;lH5K~YAEnW-ApH*sf^7)p+5 z<%SpwiWzp>zAoi?RK-heYvxSf76;O3NZHmQl_R1YjcH&-d2A)02szySqK?p$Vazf3 z4=4`)q-)ZHasfUO`NJ2pl0XErlWnU~dZ`g2uHmP>ER%?5j1*77T4|`)J>V zIrB1BgW1_|9D5yACS=3uk%G`dIYa2eReA^L!iELvXU0w|(`)BLcF>!L8Qy-PQiDkf zM5nf?SR6`PuOI5J!xo3#NZIo9N5e8&UF+a|X8ChY_V3*V+kM{6^bYJ`!CPv;1m#xJ z^@)9>#aROuTUfkClvt&A+@dQmw-?OOO=^+#W<7>Z40u{XA3$YB7W zIB2wjHRcx9U*!rh)sT=tpoUNq%{Fj&$Dbnu*4Nb;nuGL1)@5*c(Bdg<1=>=jpql-Q+zcTnmh$27 z9&cy?sTv_eFqk_}-!lY1Jj4}Jz?)KP1hG>IaOmS#Q`o4R-mG3pY`9a(ic(QktuODo zo7}ab-ZnE+nS*<~qc8Pi$nBHN=-b20Sg;1J>=Zst*D)3+LN0La>_zc)?+In3rc zkVntkAd2fLSsW)Tak1%Tpylm0J_sA)Y(8s^CND7j286o}Qq#1uTNx3T^Z%Ug4kn!P z!Zy^k)94i$>6j&Kt0ELRt*@Ty%1~wydThervAY=qN9y-GJ)S>A1e1-5NHU!5>cc)w zvKOuyi}MBb{x(a}LVmM_tH4q(hjg3 z>f0??Uj|AUB_)N-fX&<1RjSzRdY;moeomgzjKXQnMt(k9W_w2|i!~&K@r4uBes`oG z<+@yPQxt9b3lB{ZBz?^6lT>L3Y~LLfnh#g736mwtdPt)DsWA4PkOUe;=k=7%BJOPg zn+%LFc+$14^He6c1nQjf34!!W8iu3r^=b&&f?93T81DY(_pX|f3N|HRLRuK%qu#+e z4LoDU)wiO6-XiVs!QU`{{iT|qZ_RF7GruH4a+J7&P#$WQ9fd-x7?Z?+N(c1C zCaa2dwO&WObbl@J%XP?pBYMaegO4+%VfQu5w0a)oL_|&1_KPKJ10>Y%FS=NO zdZYYFKlx|2oyWijW)gK$>kn7`(~mv z6;<-(C=g+HDMSfw%=b8yXObR7CR)qm#MuDS8y7c zo+;hj{{zGqxuQGB`4*){JT#me#ritwAjV{7gU!>QjhcdVwpvP$>rv6UB14b6e~phG z0>I#a+*z{xT9|9)0e@O!MM1Sadu&h|tWAM_k2rkcgeit&1og6k0(fwE~Q zN*vNn50Mos$r@2~VKkrb*$C_4bh~Ax3kkzFAE%4@1W>6pqGl<`$WN>-PfQ5x)Vc%#j zw(w#oG!|+O6->gI^HIP7S1a7HezGwnDemah1vu;;UrX zCJwijA|yDH(jf++N0BG8)Mrt8ObbDK+{Jek+<>1GE)wA0>&^g=$Z*}|%u|wyJqFIWI zy=R9A^l@nrlX65I)&fpY@P_(dX$w7oX`hCr4JF}_RsnJ1KK<%i-n72>>)XsFi{HPY zM{gQi(~PE0>RT~!+tVnhA5spLNRoetIgxm!{@D~2zv+YfH=TLJUN+&FJROYDSO@*& z#H{9S1691eu`5&b3!))kwl;k(U~2pk-}J}f+wEf^>S$mlapo(qV2zXIQqo*yd9u!n zxg!Sq@$5S9%jDfM;xuD1%9+P0!M3$$uL8lG%!z@ zYibg-h;L{VR06gxh?i}vG1O;%a!)UhM_Jn*eyLuAS>KtQTCoWHH6@cO9JetT_M0#g z&WcCIxZpHGC0l!Txxpj|+pgUYWwj4A2HjTA;hV$#_7v952QzssXj+oE|AdJ{s@y1| z>)4Ady-35d4hKEjZ{KYn(@BNX?@jD}BK8yBvgRn4@KSGB!B^I~e^%p`f_!8)d#BAq z&a^BKwY-}f&nI|O7}}KCnh;(^@+>(ftdqL2G&i0Z^)4n!$}zOOD@)u&snxP!RGBz{ z-f9sZX+duqvq^DOyWLnVS&2d0TUaZUx(p+wY{-sU5}a~h!Oy1*n4R#Hf6P4;Md?~$bM-sScK zuHW&Y-cZR-Cz7^3pMda}@C={>dcWxh zd$-@-B%W3GzgU_gqD^x;lXbMCr}IK8hZNd0eQ9bqYv;Lot##6KhRQkXDHj}Z;m}qQ zD!xE*id)c$oEyWSx0Kn{RNN;&4|MRGCl`@+Zh&Pa|JKU$%jedx$8LfFvJEwnkCAEg zy7A9uzm+p+)HoyiO-R0^H!TNzlkHU;%HP zC>G{)rd~@luRz>!h=u6Q#Oo+?MhW{XW*KTn_MMAXnv-+bvsLSUdnAbGX1`P2p!&nb z&C-wJgV!7r_(!ZFmComXgWQ1Zu z!R?4DAV|Zzu|>DvG5CF?gHz|`z1-TQ9sOU9)gT8iigqsRt);)BDG;S2TyGD|aYwky zbzaMJpUTF_9yYLZ!Qs2`ga&0|cI@=WO|&b%0!X~iiHI2KpgBtTThvP)hf(?*{HTR) zF0%6TN{TP0c)y1*)n0v&Q>+Uss%s%uKHn4UK7LXjeXjmW2(&$}s?@`!n7}wGAK(8ywve5oPoc_-@Qd(@3i65w3J41b ziSP=`$_tCi%E$@H0cGTbWk5hlI`RMg#W)Fz78xj%R#$~i0;icBQ`XTS9y;&x%Z3Rm z^CKYok|(OZwqW1%6kB~;N%jWMPsw>0nArBSboh*pI0FEJnem@C8?$mR?r6K=wzCkfONY2}h7XS##A? 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Delineation of the boundary of the region of interest for the model. Boundary data is provided as a shapefile and converted into a `sspm_boundary` object with a call to `spm_as_boundary()`. 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. -3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e. harvest). +3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth` function use spatiotemporal GAM models to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. 5. The `spm_aggregate_catch` function aggregates catch into patches and years, and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year, divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. From e34e964caf120142190b437e28b9e687d773ed8c Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:35:46 -0500 Subject: [PATCH 040/118] closes #89 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index d89b2aee..c40ca8c1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -71,7 +71,7 @@ The key workflow steps are: 1. Delineation of the boundary of the region of interest for the model. Boundary data is provided as a shapefile and converted into a `sspm_boundary` object with a call to `spm_as_boundary()`. 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). -4. The `spm_smooth` function use spatiotemporal GAM models to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. +4. The `spm_smooth` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. 5. The `spm_aggregate_catch` function aggregates catch into patches and years, and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year, divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. From b16b1a24378596131e9c58c045a076675f9e562f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:36:28 -0500 Subject: [PATCH 041/118] closes #90 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index c40ca8c1..739e4ab6 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -72,7 +72,7 @@ The key workflow steps are: 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. -5. The `spm_aggregate_catch` function aggregates catch into patches and years, and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year, divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. +5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year, divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. 8. Predictions from the fitted model can be obtained using the built-in `predict()` method, and plots with the `plot()` method. From 45594b08c53432f3fceaf432c42129f7e09bc958 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:37:07 -0500 Subject: [PATCH 042/118] closes #91 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 739e4ab6..de90e1a9 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -72,7 +72,7 @@ The key workflow steps are: 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. -5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year, divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. +5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. 8. Predictions from the fitted model can be obtained using the built-in `predict()` method, and plots with the `plot()` method. From 7afa766504b382bf18df042f552bff4481b13fc0 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:39:26 -0500 Subject: [PATCH 043/118] closes #92 + addresses a point brought up in the issue above --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index de90e1a9..5ec72651 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -71,9 +71,9 @@ The key workflow steps are: 1. Delineation of the boundary of the region of interest for the model. Boundary data is provided as a shapefile and converted into a `sspm_boundary` object with a call to `spm_as_boundary()`. 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). -4. The `spm_smooth` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. +4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. 5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. -6. The `sspm` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. +6. The `sspm()` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. 8. Predictions from the fitted model can be obtained using the built-in `predict()` method, and plots with the `plot()` method. From 25c2c93a6955cbc72d4be8a3f3f52d7fa4983581 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:41:01 -0500 Subject: [PATCH 044/118] closes #93 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 5ec72651..b26b1d63 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -75,7 +75,7 @@ The key workflow steps are: 5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm()` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. -8. Predictions from the fitted model can be obtained using the built-in `predict()` method, and plots with the `plot()` method. +8. Predictions from the fitted model can be obtained using the built-in `predict()` method. Plots can be generated with the `plot()` method. ![The sspm workflow.\label{fig:workflow}](../man/figures/flowchart.png){width=90%} From e839214f2f4e24f2635b236a47b6dcae4c762f30 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:42:12 -0500 Subject: [PATCH 045/118] closes #94 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index b26b1d63..86ba1b9c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -81,7 +81,7 @@ The key workflow steps are: # Acknowledgements -This research was supported by the canadian Department of Fisheries of Oceans (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. +This research was supported by Fisheries and Oceans Canada (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. # References From f02321dc1276f3925daf76b48b4b6a4f847b6b0b Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:43:26 -0500 Subject: [PATCH 046/118] closes #95 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 86ba1b9c..af236d00 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -81,7 +81,7 @@ The key workflow steps are: # Acknowledgements -This research was supported by Fisheries and Oceans Canada (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. +This research was supported by Fisheries and Oceans Canada's (DFO) Sustainable fisheries Science Fund and by a Discovery Grant from the canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. # References From 438c6d79a236cde71958e116421ce0cb3c174d08 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:47:00 -0500 Subject: [PATCH 047/118] closes #96 --- paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 31ffdd7e..4998c42f 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -108,7 +108,7 @@ @article{berkes_implementing_2012 } @article{crowder_impacts_2008, - title = {The {Impacts} of {Fisheries} on {Marine} {Ecosystems} and the {Transition} to {Ecosystem}-{Based} {Management}}, + title = {The {impacts} of {fisheries} on {marine} {ecosystems} and the {transition} to {ecosystem}-{based} {management}}, volume = {39}, url = {https://doi.org/10.1146/annurev.ecolsys.39.110707.173406}, doi = {10.1146/annurev.ecolsys.39.110707.173406}, From e2f1f16d7f1fe726ee4c3dd1315dfd13a655e3af Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:49:19 -0500 Subject: [PATCH 048/118] closes #97 closes #100 --- paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 4998c42f..f4395d56 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -244,7 +244,7 @@ @book{wood_generalized_2017 @techreport{dfo_assessment_2019, type = {techreport}, institution = {Canadian Science Advisory Secretariat (CSAS)}, - title = {An assessment of {Northern} {Shrimp} (\textit{{Pandalus} borealis}) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} (\textit{{Pandalus} montagui}) in shrimp fishing area 4 in 2018}, + title = {An Assessment of {Northern} {Shrimp} (Pandalus borealis) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} (Pandalus montagui) in Shrimp Fishing Area 4 in 2018}, number = {2019/027}, author = {{DFO}}, year = {2019}, From ac2423c2735496e19efaacedc2ef6695c1854ee5 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:52:26 -0500 Subject: [PATCH 049/118] closes #98 --- paper/paper.bib | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index f4395d56..60ff4f05 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -29,7 +29,8 @@ @article{karp_accounting_2019 editor = {Maravelias, Christos}, month = apr, year = {2019}, - pages = {fsz048}, + pages = {1305--1315}, + volume = {76}, file = {Karp et al. - 2019 - Accounting for shifting distributions and changing.pdf:/home/vlucet/Zotero/storage/JP6BDALA/Karp et al. - 2019 - Accounting for shifting distributions and changing.pdf:application/pdf}, } From 9c24a470c9e97a4b7eaa16cf8b98bd0639684bab Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 17:55:25 -0500 Subject: [PATCH 050/118] closes #101 --- paper/paper.bib | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 60ff4f05..8951db09 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -253,9 +253,8 @@ @techreport{dfo_assessment_2019 } @techreport{pedersenNewSpatialEcosystembased2021, - type = {Can. {Sci}. {Advis}. {Sec}. {Res}. {Doc}.}, - title = {A new spatial ecosystem-based surplus production model for {SFA} 4-6 {Northern} {Shrimp}}, - number = {2022/nn}, + type = {Canadian Science Advisory Secretariat (CSAS) Research Document}, + title = {A New Spatial Ecosystem-Based Surplus Production Model for {SFA} 4-6 {Northern} {Shrimp}}, author = {Pedersen, Eric J. and Skanes, Katherine and Le Corre, Nicolas and Koen-Alonso, Mariano and Baker, Krista}, year = {2022}, pages = {in press}, From 8de9f13aaef0e2e991fc00c5ad28642c1856c0c7 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sat, 26 Nov 2022 18:01:15 -0500 Subject: [PATCH 051/118] closes #99 --- paper/paper.bib | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 8951db09..ca8ae645 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -147,10 +147,12 @@ @article{zhang_longer_2021 @article{prager_suite_1994, title = {A suite of extensions to a nonequilibrium surplus-production}, + journal = {Fishery Bulletin - National Oceanic and Atmospheric Administration}, language = {en}, author = {Prager, Michael H}, year = {1994}, - pages = {17}, + volume = {92}, + pages = {374--389}, file = {Prager - A suite of extensions to a nonequilibrium surplus-.pdf:/home/vlucet/Zotero/storage/4HHXRZ4Y/Prager - A suite of extensions to a nonequilibrium surplus-.pdf:application/pdf}, } From ad9eb70ee8aa7f6e2796d2d43409cb28a9b9a920 Mon Sep 17 00:00:00 2001 From: VLucet Date: Sat, 26 Nov 2022 23:04:12 +0000 Subject: [PATCH 052/118] [Bot] build pdf --- paper/paper.pdf | Bin 521890 -> 521603 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 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z^nGgblZ1UH9LD(F#7LX)2!4t^6%aK%^S+@mvsMX|rT(D$GRnxc?bA ztN`9vdBNOJZ{@dGa7k@>5AE)U7|&;@@MnB@5p??R>q)*kz{xCcq)`_k@H%%AhHu71 zUL!v8Ixmmy@h%Kcc^t}?n~FJEP83y*2dO3imtt~7ErY|)tBt6iiTEt7v-W|uX?teP zFV~yP`8lcuF~XgVIdKi9Q=b0s;vVLX&MbB&5qNsZH~h@`Ln}&-wHjItZIFDwTmL$K z*Bz3*_b{wke&ob{zQmTPoNNp+M7H+f-TEJ?3Iyqq^S2k{d^bZRrSnDp)nw;KSdPr` z`K{Yg^Tu2LS&1|Ws{2RpCmlUe0}_xED)8W*h%ix?7bnMFaM?8bL%4E#1-?yjqGx2F z0d8l792!{4|K?r~PS(m1{{y&qINCX-&oBrWk^D>u;ab_p^q*h{-+c#Oi2|)SwY=*?!s_{^`H+AcGQ@ky zPqy1>mF?LW&$KV&PVGC^(+sX@*Ha`1ud72c#t!Y^?8Cjl1I-7#(?a5(1mlmmV@Oy9 Uvdbnw{)Eo2;#XESLn8S90|regxc~qF From 1adfdbe8ac2229b310227133e49c459b361d5c85 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 22:54:37 -0500 Subject: [PATCH 053/118] closes #104 closes #105 --- paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index af236d00..695438a9 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,9 +43,9 @@ Resource managers are becoming increasingly interested in how variation in ecosy -Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. - -Two limitations of classic SPMs is the assumption of spatially constant productivity, and that stock productivity is affected only by stock abundance and fishing. These assumptions ignore the effect of global changes that are affecting fisheries, such as climate change, that affect the growth rates of stocks independently of fishing pressure.The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change and to predict how fishing pressure and changing environmental conditions may affect future abundance. In the context of climate change and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. +Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. SPMs typically model spatially constant productivity. They also assume that populations are only affected by past abundance and fishing, which ignores stressors like climate change which affect growth rates independently of fishing pressure. + +In the context of global warming and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change and to predict how fishing pressure and changing environmental conditions may affect future abundance. 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zDSl4SxGYRdYS2v=8K%?O?NmvM>4xA)z_5grOx!yN8w7OR|G3Y3LL?6XVQ4GC?!>BU zqEUrx$Dxa1QH^{)EXT$G1spL3A?esJNltQ*Bi`wPv<`K-C0w+&!*R3vOH~5F&GsQpET)M5r}HF}S;>;P=4KOmj6#VrI** zm$eJ^mT?K*ExmO?YLh9wayl^OrpUA1W;zs?(V|a-#S5>og7Le?MZOSvLrLxQ0Bw{i z1F09#SZBpE?W*F7RC*4|aV`WwE__oDw@sLq4(s&G5YGlmx@lzJk=~?dx6&d^doIjf zw^50uTUaHM{%7w!`Xp@?sft&+V##xq2kDhfx>MX%!5v)zcM$^qTtt)}a$Y3q%@0Ih zFWVj(Oh5*p+fr4bLLa%Uq#k-Mb-SWjh3mj7dPDUr z1t2wp+#f(mmX&PM@mAwGzN3fKRq|ApbHN5)ruaZ(w$uXa&ZF;+Rp%6#?K~F0SuW}o zJ?(nc+hx%EYW^4f&XKu!>fBJtCAnkN9pMhwO>CEf2BEQQboI`_EN~}XWaQ6;(~l?E b9uRet!vh%h7C?%?%fX32O)aS`h4B9YljPd4 From a8a967e06f285eea649e340f541fced1df9bd0f9 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:01:23 -0500 Subject: [PATCH 055/118] closes #106 --- paper/paper.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 695438a9..0b34bc65 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -49,7 +49,11 @@ In the context of global warming and shifting ranges, fisheries productivity is -Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. This allows for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. +Population models like SPMs usually fall under the two following categories: process-based and statistical models. Process-based models often rely on differential or difference equations and are based on replicating the underlying processes (e.g., predation, recruitment, dispersal) driving population dynamics. Statistical models instead fit a regression model to time series of population abundances, abundance indices, or productivities, with some assumed error distribution for variation around predictions. + +We have chosen a statistical approach to fitting SPMs. Statistical models allow for estimation of parameter uncertainty and ranges of model predictions and for flexibly incorporating potential ecosystem drivers into models [@plaganyi2014Multispecies]. Statistical models also allow for straight-forward estimation of spatial variation in population parameters such as maximum productivity or density dependence from data, in the absence of theory predicting how these parameters should vary. + +In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 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z-^mtOf=eK*qFMxC$27Vn)YqR!KW@OYPT51JeAvK Date: Mon, 28 Nov 2022 23:04:17 -0500 Subject: [PATCH 057/118] closes #107 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 0b34bc65..9dfeaf84 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,7 +55,7 @@ We have chosen a statistical approach to fitting SPMs. Statistical models allow In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to estimate spatiotemporally varying biomass, and to estimate SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. +While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). 2. 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closes #108 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 9dfeaf84..aa08f450 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,7 +55,7 @@ We have chosen a statistical approach to fitting SPMs. Statistical models allow In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manage biomass and harvest data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. +While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. 1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). 2. Spatiotemporal smoothing of biomass and environmental predictors using GAMs. From 75f4f3111507efb2b106b3483b74d266fe8212d5 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:10:49 -0500 Subject: [PATCH 060/118] closes #109 closes #110 --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index aa08f450..9297b06a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -60,9 +60,9 @@ While the initial application of this model was modelling Newfoundland and Labra 1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). 2. Spatiotemporal smoothing of biomass and environmental predictors using GAMs. 3. Computation of surplus productivity based on biomass density and fishing effort. -4. Fitting of SSPMs to productivity data with GAMs. +4. Fitting of SSPMs to productivity estimates with GAMs. 5. Visualization of results, including confidence and prediction intervals. -6. One-step-ahead prediction of biomass for model validation and scenario-based forecasting. +6. One-step-ahead projections of biomass for model validation and scenario-based predictions. Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general. From 1e43da00bd4466e806dc0e1f4185e435ba966528 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:12:23 -0500 Subject: [PATCH 061/118] closes #111 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 9297b06a..73d0f1f5 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -68,7 +68,7 @@ Although it was developed in a fisheries context, the package is suited to model # Package design -The package follows an object oriented design, making use of the S4 class systems. The different classes in the package work together to produce a stepwise workflow (\autoref{fig:workflow}). +The package follows an object oriented design, making use of the S4 class systems to model a stepwise workflow: (\autoref{fig:workflow}). The key workflow steps are: From c3a8d154a83fa765f845a05ba5aa219c647559bd Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:25:31 -0500 Subject: [PATCH 062/118] closes #112 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 73d0f1f5..49cab0c8 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -81,7 +81,7 @@ The key workflow steps are: 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. 8. Predictions from the fitted model can be obtained using the built-in `predict()` method. Plots can be generated with the `plot()` method. -![The sspm workflow.\label{fig:workflow}](../man/figures/flowchart.png){width=90%} +![The sspm workflow. Gray cylinders represent raw, unprocessed sources of data. Each blue diamond shape represents a function processing a raw input and validating it, or producing an intermediate package object, represented as a green object. Secondary objects like formulas, which must be created by the user, are represented by a purple document shape. Finally, outputs are represented by a red document shape. The steps of the workflow as described above are denoted by dotted lines and corresponding step number. \label{fig:workflow}](../man/figures/flowchart.png){width=90%} # Acknowledgements From eb350eff6e65877d309ac109944badfeba7c6599 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:33:14 -0500 Subject: [PATCH 063/118] closes #114 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 49cab0c8..016be08f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -72,7 +72,7 @@ The package follows an object oriented design, making use of the S4 class system The key workflow steps are: -1. Delineation of the boundary of the region of interest for the model. Boundary data is provided as a shapefile and converted into a `sspm_boundary` object with a call to `spm_as_boundary()`. +1. Provided boundary data in the form of a shapefile is converted into a `sspm_boundary` object using `spm_as_boundary()` to define the boundary/region of interest. 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. From 583967a47d47cfc60f7e332f1db81cf53706dbda Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:46:55 -0500 Subject: [PATCH 064/118] add log and tex to gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index e1a76dcb..5a51988b 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,5 @@ docs/ pkgdown/ *.rds inst/doc +*.log +*.tex From 3b8ed9c0853382ec436c92ab25095723fff8c98b Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 28 Nov 2022 23:56:26 -0500 Subject: [PATCH 065/118] closes #116 --- paper/paper.md | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 016be08f..f07e7d28 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,14 +55,7 @@ We have chosen a statistical approach to fitting SPMs. Statistical models allow In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow}. - -1. Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). -2. Spatiotemporal smoothing of biomass and environmental predictors using GAMs. -3. Computation of surplus productivity based on biomass density and fishing effort. -4. Fitting of SSPMs to productivity estimates with GAMs. -5. Visualization of results, including confidence and prediction intervals. -6. One-step-ahead projections of biomass for model validation and scenario-based predictions. +While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow} and in the next section. Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general. @@ -72,14 +65,23 @@ The package follows an object oriented design, making use of the S4 class system The key workflow steps are: -1. Provided boundary data in the form of a shapefile is converted into a `sspm_boundary` object using `spm_as_boundary()` to define the boundary/region of interest. -2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. -3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). -4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. -5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. -6. The `sspm()` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. -7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. -8. Predictions from the fitted model can be obtained using the built-in `predict()` method. Plots can be generated with the `plot()` method. +* Discretization and aggregation of spatially structured observations into discrete patches, with a range of methods of discretization (random or custom sampling, Voronoi tessellation, or Delaunay triangulation). + 1. Provided boundary data in the form of a shapefile is converted into a `sspm_boundary` object using `spm_as_boundary()` to define the boundary/region of interest. + 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. + +* Spatiotemporal smoothing of biomass and environmental predictors using GAMs. + 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). + 4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. + +* Computation of surplus productivity based on biomass density and fishing effort. + 5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. + 6. The `sspm()` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. + +* Fitting of SSPMs to productivity estimates with GAMs. + 7. The `spm()` function is used to fit a SSPM model to the output of step 6, using a GAM model with custom syntax able to model a range of SSPMs. The output is an `sspm` object. + +* Visualization of results, and one-step-ahead projections of biomass for model validation and scenario-based predictions. + 8. Plots can be generated with the `plot()` method. Predictions from the fitted model can be obtained using the built-in `predict()` method, including confidence and prediction intervals ![The sspm workflow. Gray cylinders represent raw, unprocessed sources of data. Each blue diamond shape represents a function processing a raw input and validating it, or producing an intermediate package object, represented as a green object. Secondary objects like formulas, which must be created by the user, are represented by a purple document shape. Finally, outputs are represented by a red document shape. The steps of the workflow as described above are denoted by dotted lines and corresponding step number. \label{fig:workflow}](../man/figures/flowchart.png){width=90%} From 7e4672cd340d7f4f2354075ee361bf405639326d Mon Sep 17 00:00:00 2001 From: VLucet Date: Tue, 29 Nov 2022 04:57:47 +0000 Subject: [PATCH 066/118] [Bot] build pdf --- paper/paper.pdf | Bin 522115 -> 521850 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 6e12a8aaf198f07b5acb42f2e1a8a35c37bd2e09..2246e15164a0ba663f2b4c43cb776a06525c5e93 100644 GIT binary patch delta 27204 zcmV)6K*+y??;rZ?AAqz0|5pJumRJEO128i%mw*!n6qi3(0R?}#k-gVf#19mk`vL?K z@XCxBOy7E@ZVjCzNEbDPnTvj3T#aU=yNA1nEqf9y%$??=5OtjI!3>u?!UMG zzoP+u{QUOIQ%HYv6WTK`e7ODm>*j=h{&D;L&bc(4D;58j@#Esp=HpL4e_dJFgQvR!mxdGsZJ3eWgwaLF?QE{g}TJ#^zs4V9vJ3zx?+f;`a5AIQDP5V|m84 zy?u9%GfC+bSg_J+N+!#EeB_j5PMAg}!#|`?|ErJhO38otlm3by8$ZwZ@$s8TNFCC* zIf<+p!2rpJhjebQ0*AW#u?$SU(CF;s$TSNJsf3>ki 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Population models, in particular fisheries productivity models, rarely integrate important spatially-structured ecosystem drivers -2. The Northern Shrimp stock in the Newfoundland and Labrador Shelves currently lacks a population model -3. Current SPM models are rarely spatially explicit and usually cannot account for relevant ecosystem drivers -4. Fisheries managers lack user-friendly, flexible tools to implement and apply SSPMs +Population models are important tools for making management decisions, especially in fisheries, where predictive methods like Surplus Productivity Models (SPMs) are widely used. Fisheries analysts and managers often lack user-friendly, flexible tools to implement and apply SPMs. In addition, SPMs are rarely spatially explicit and usually cannot account for relevant ecosystem drivers. Therefore, there is a need for tools that implement spatially explicit surplus production models (SSPMs). The Northern Shrimp stock in the Newfoundland and Labrador Shelves is an example of a stock in need of an SSPM that can integrate important spatially-structured ecosystem drivers. # Summary From fbe27834153d3e256736c823d375dd380593244e Mon Sep 17 00:00:00 2001 From: VLucet Date: Tue, 29 Nov 2022 18:24:21 +0000 Subject: [PATCH 068/118] [Bot] build pdf --- paper/paper.pdf | Bin 521850 -> 521902 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 2246e15164a0ba663f2b4c43cb776a06525c5e93..4344d724ee1112ea876318820a1484adec6c6924 100644 GIT binary patch delta 26227 zcmV)4K+3=R>>sY}AFv+_12!}>lOhWuf9?w)JV3AXH1OQoQ-BYHIh8T?p~}Yi;=k|U z77UV^smxTlVHgixuO(4jB7?zLJ_ac^G5GJl{w=<+>7W1fg~vC1;)c(Xt4VM^YcHnn zKfnCr%WRote#$SG_G4%Vwb<;Y@)tTLA4&I@(*K1F@c!+~Zx1f?#Fd*JcYgZzfA>{_ zzWtp3dSy&##-xN-0e=?W>$kss`|}qzJlMy#t!MikT7=4fT4-5mR&o}`OVZ^O5_mot++{~1jF4nSHbey!a;2MZ(IB8~Q z)MPwNisglq5;&EnQPwjXH`df6YiR8u(wP6pmeGhhA$n%3sa935%}%l&7@{;8P^R% 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-28,10 +28,7 @@ affiliations: # Statement of need -1. Population models, in particular fisheries productivity models, rarely integrate important spatially-structured ecosystem drivers -2. The Northern Shrimp stock in the Newfoundland and Labrador Shelves currently lacks a population model -3. Current SPM models are rarely spatially explicit and usually cannot account for relevant ecosystem drivers -4. Fisheries managers lack user-friendly, flexible tools to implement and apply SSPMs +Population models are important tools for making management decisions, especially in fisheries, where predictive methods like Surplus Productivity Models (SPMs) are widely used. Fisheries analysts and managers often lack user-friendly, flexible tools to implement and apply SPMs. In addition, SPMs are rarely spatially explicit and usually cannot account for relevant ecosystem drivers. Therefore, there is a need for tools that implement spatially explicit surplus production models (SSPMs). The Northern Shrimp stock in the Newfoundland and Labrador Shelves is an example of a stock in need of an SSPM that can integrate important spatially-structured ecosystem drivers. # Summary From 92f48716bfd98fd27b7330e28538fcc2b586e6f7 Mon Sep 17 00:00:00 2001 From: VLucet Date: Tue, 29 Nov 2022 20:49:35 +0000 Subject: [PATCH 070/118] [Bot] build pdf --- paper/paper.pdf | Bin 521902 -> 521902 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 4344d724ee1112ea876318820a1484adec6c6924..b2d68f172f5e7cb8a5f616ea1370d033d206ae20 100644 GIT binary patch delta 239 zcmZ4YR({=E`Gyw87N!>F7M2#)Eo`6TIgJdgOf0R845OwqCbDU4S4m)FX5xSePS;Oj zGePjOli1AQyzMKK*d!QLl1&UPER4*|brVxf40TP6jV*O85-p5$&43cdmZp|TK(hU5 zGTZj2$?UAXY(@qqmPUrtKYU;phqAZxeq@(mb~AS|Gq!MaGIF(aHZ?YLbTx8xbagXy eGIumGHg^VUu~V=itR!}NpcF7M2#)Eo`6TISnnWj7+Qy4WgzqCbDU4S4m)FX5xSePS;Oj zGePjOli1AQyzMKK*d!QL%!~{SOihwgbrVw)jde{dP1AG}fnq=|P|(EC!Xm{yvHfW> z+xDl)?5w?Ph89L9h6d9=d|(%cvbXboWS3xeGqf~wGd8g>a Date: Tue, 29 Nov 2022 15:53:23 -0500 Subject: [PATCH 071/118] closes #62 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 422c06b8..3013b1af 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -70,7 +70,7 @@ The key workflow steps are: 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. -* Computation of surplus productivity based on biomass density and fishing effort. +* Computation of surplus production based on biomass density and fishing effort. 5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. 6. The `sspm()` function combines productivity and predictor datasets into a single dataset. Additionally, the user may create lagged versions of predictors with `spm_lag()` and split data into testing and training sets for model validation with `spm_split()` at this stage. From fe0ee11e681f80dd67a738c228e33ccecb52f63d Mon Sep 17 00:00:00 2001 From: vlucet Date: Tue, 29 Nov 2022 15:55:15 -0500 Subject: [PATCH 072/118] closes #61 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 3013b1af..e7e69eee 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -42,7 +42,7 @@ Resource managers are becoming increasingly interested in how variation in ecosy Surplus production models (SPMs) are one of the classic models used in fisheries and are based on modelling changes in the total biomass of a stock in a given location over time as a function of current stock abundance and fishing pressure [@walters_surplus_2008]. Classically, SPMs assume single unstructured stocks with purely logistic dynamics [@walters_surplus_2008] and, as such, have been of limited use for modelling more complex stocks. They are useful in data-poor contexts where the age structure of the population is not accessible or when age or length structure do not change substantially over time [@prager_suite_1994; @punt_extending_2003]. SPMs typically model spatially constant productivity. They also assume that populations are only affected by past abundance and fishing, which ignores stressors like climate change which affect growth rates independently of fishing pressure. -In the context of global warming and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change and to predict how fishing pressure and changing environmental conditions may affect future abundance. +In the context of global warming and shifting ranges, fisheries productivity is likely to be a moving target [@karp_accounting_2019], and managers need better methods that account for varying productivity [@szuwalski_climate_2016]. The Northern Shrimp (*Pandalus borealis*) in the Newfoundland and Labrador Shelves, which has undergone several periods of large-scale biomass change in the last two decades, despite a relatively constant harvest regime, is a prime example of a population thought to be affected by environmental conditions [@dfo_assessment_2019]. These populations currently lack a population model to understand the drivers of this change and to predict how fishing pressure and changing environmental conditions may affect future abundance, which managers are advised to account for. From 649c5e8250daa30796117177782a0ba13ac51aa4 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 11:11:04 -0500 Subject: [PATCH 073/118] closes #113 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index e7e69eee..77f54db4 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -68,7 +68,7 @@ The key workflow steps are: * Spatiotemporal smoothing of biomass and environmental predictors using GAMs. 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). - 4. The `spm_smooth()` function use spatiotemporal GAMs to smooth the biomass and predictor data, based on the spatial structure from `sspm_discrete_boundary`. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. + 4. The `spm_smooth()` uses GAMs to calculate spatially smoothed yearly estimates of biomass and environmental predictors for each patch from trawl-level data, based on the spatial structure from the `sspm_discrete_boundary` object. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. * Computation of surplus production based on biomass density and fishing effort. 5. The `spm_aggregate_catch()` function aggregates catch into patches and years and calculates patch-specific productivity for each year as the ratio of estimated biomass density plus catch from the next year divided by estimated biomass density of the current year. The result is returned as a `sspm_dataset`. From 75dcaa123a827d3cc5429a6683385468914c2874 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 11:16:15 -0500 Subject: [PATCH 074/118] closes #115 --- paper/paper.bib | 8 +------- paper/paper.md | 2 +- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index ca8ae645..ff2d1b95 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -204,7 +204,6 @@ @book{gotelli_primer_2008 file = {Snapshot:/home/vlucet/Zotero/storage/L24AL4DI/a-primer-of-ecology-9780878933181.html:text/html}, } - @article{thorson_importance_2015, title = {The importance of spatial models for estimating the strength of density dependence}, volume = {96}, @@ -220,7 +219,6 @@ @article{thorson_importance_2015 pages = {1202--1212}, } - @book{walters_fisheries_2004, address = {Princeton, N.J., U.S.A.}, title = {Fisheries ecology and management}, @@ -259,11 +257,9 @@ @techreport{pedersenNewSpatialEcosystembased2021 title = {A New Spatial Ecosystem-Based Surplus Production Model for {SFA} 4-6 {Northern} {Shrimp}}, author = {Pedersen, Eric J. and Skanes, Katherine and Le Corre, Nicolas and Koen-Alonso, Mariano and Baker, Krista}, year = {2022}, - pages = {in press}, + url = {https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html} } - - @article{plaganyi2014Multispecies, title = {Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity}, volume = {15}, @@ -279,5 +275,3 @@ @article{plaganyi2014Multispecies year = {2014}, pages = {1--22}, } - - diff --git a/paper/paper.md b/paper/paper.md index 77f54db4..ef89b419 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -52,7 +52,7 @@ We have chosen a statistical approach to fitting SPMs. Statistical models allow In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`. -While the initial application of this model was modelling Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021], the R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) easier to estimate and apply to any spatially structured stock. The package uses GAMs to smooth spatiotemporally varying biomass, and to fit SSPMs based on changes in fitted biomass, observed catch, and spatially structured environmental predictors. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow} and in the next section. +The R package `sspm` is designed to make spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The basic model it implements was first used to model time-varying production in Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021]. However, the general modelling approach used here will work for any spatially structured fishery with sufficient data. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow} and in the next section. Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general. 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z8RFGZwdPUYW#sLXX=HBjQ%?#61?K4<2cPW$^#7Ims`~&s7~a33WE|{&W0Kz#Z%i`( ze{B;S5+_G@DGN6D&k5~5&x3(?)}&F0^DaNf5d&jFm6 z|G7F72aCT9dBN`IfD#xkUNG4OfEtFEA0l}HsDh#6;N<6I=i+}O{23_Nl$^|^Ox!If z=%oZW**Q5m{!Z@4qG5sT)vGTR>r{3rwb$`hrpsA3jAULDPU zdSxh+S%yR6`WnUw)F!5+t(g>`=$I3W>YxnLM94hnWODA5xY&pZ#_4!uM`CUY!=MCt z!*_~lK!-*tvtrTs6eNfDxE|6#DYCQ3R>*8~Oq|52BXgbn6oG9$jxZ2hVNfmNxeCYQ zN{pEx&iV9f)EOYa7_FP9eZ_1WBg+<*C|OOwL!EQI{TZ+L>Sp3|AwRoP|F$AQy5nW; zskPFn)>qSUkv;#1mtgC8@)~k7Ii?v9r}g@=D)HXT<)As7>1)Bv`I^aAx^9X? z&}pl~^NOYn*Ij|{Q`2r};!ZWM>RALNd2l5qYUCHtK$Thn83hQjY!+ zT_(ULKsZyn)a;2=S$^w?Bk=qlmFp<(xkiM08SnUdw05wWRKVNy{4mp~+AUGf2I30E zb@~g1c1AxPFSFg%?Ovt%s(!90X~hDV78&hzYoq{9k^SnVn|?7r>$(p)YUE;PaoU}_ zIpN1I@v*kjN)v@v9}@8XYu{l0j}Di7Z?)2Q04g*-4hjPbHgy|c3knWC4)Eq30N0e4 z@1JcW?`Y}tmY4skBaoMROL-w)DIOr7xCEy(za%%1M_igyTwGd)LxNYF6UZz1mZKv7 zKPCqMk&-;A) zWON-;sc?yuj^)`DL%1#m4XSlQ8pc;Rhkky@5h0MJrTu#%sCjXJvUsBy!*tGLtK_| z>Bcw4FscqB)$cIrCx7)B?|HoE_oeAZVbc_K6)8Rk^&j-eBi`N+$MeX;bB=S?$oJP8 zPK&vI6}@&GPa6I<7)GZZM(33u{HrlV0o|flL>5Ef@J9d{G6x4Y5Q&ynT15uw{{YUW B!9@T7 From d1ddf256e07b1da446a27eb1deb0d47cd81de633 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 12:42:11 -0500 Subject: [PATCH 076/118] fix plot sizes, closes #118 --- vignettes/An_example_with_simulated_data.Rmd | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/vignettes/An_example_with_simulated_data.Rmd b/vignettes/An_example_with_simulated_data.Rmd index 91634c71..7a656189 100644 --- a/vignettes/An_example_with_simulated_data.Rmd +++ b/vignettes/An_example_with_simulated_data.Rmd @@ -2,7 +2,7 @@ title: "An example with simulated data" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{An_example_with_simulated_data} + # %\VignetteIndexEntry{An example with simulated data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -31,7 +31,7 @@ catch_simulated 1. The first step of the `sspm` workflow is to create a `sspm_boundary` from an `sf` object, providing the `boundary` that delineates the boundary regions. The object can then be plotted with `spm_plot` (as can most `sspm` objects). -```{r boundaries} +```{r boundaries, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} bounds <- spm_as_boundary(boundaries = sfa_boundaries, boundary = "sfa") @@ -86,7 +86,7 @@ bounds_delaunay 5. Plotting the object shows the polygons that have been created. -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(bounds_voronoi) ``` @@ -121,12 +121,12 @@ biomass_smooth 8. The smoothed results for any smoothed variables (listed in "smoothed vars" above) can be easily plotted: -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(biomass_smooth, var = "weight_per_km2", log = FALSE, interval = T) ``` You can also make a spatial plot -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(biomass_smooth, var = "weight_per_km2", use_sf = TRUE) ``` @@ -211,7 +211,7 @@ sspm_model_fit 16. Plotting the object produces a actual vs predicted plot (with TEST/TRAIN data highlighted. -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(sspm_model_fit, train_test = TRUE, scales = "free") ``` @@ -236,17 +236,17 @@ head(biomass_one_step) 18. We can produce an array of plots, as timeseries or as spatial plots -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(sspm_model_fit, log = T, scales = 'free') plot(sspm_model_fit, log = T, use_sf = TRUE) ``` -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(sspm_model_fit, biomass = "weight_per_km2_borealis", scales = "free") plot(sspm_model_fit, biomass = "weight_per_km2_borealis", use_sf = TRUE) ``` -```{r} +```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(sspm_model_fit, biomass = "weight_per_km2_borealis", next_ts = TRUE, aggregate = TRUE, scales = "free", smoothed_biomass = TRUE, interval = T) From dd654fc09211ee107bf40ce97de6b5edf9e54547 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 12:54:21 -0500 Subject: [PATCH 077/118] closes #119 --- vignettes/An_example_with_simulated_data.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/An_example_with_simulated_data.Rmd b/vignettes/An_example_with_simulated_data.Rmd index 7a656189..66010639 100644 --- a/vignettes/An_example_with_simulated_data.Rmd +++ b/vignettes/An_example_with_simulated_data.Rmd @@ -119,7 +119,7 @@ biomass_smooth <- biomass_dataset %>% biomass_smooth ``` -8. The smoothed results for any smoothed variables (listed in "smoothed vars" above) can be easily plotted: +8. The smoothed results for any smoothed variables (listed in "smoothed vars" above) can be easily plotted. Here the 4 panels represent 4 different patches in the same SFA. The panels show a very similar pattern due to the nature of the simulated data we are using. The data points are actually different, but the differences are not noticeable in this example case. ```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} plot(biomass_smooth, var = "weight_per_km2", log = FALSE, interval = T) From 8f71f246c14b00fd256d842786472d1d75ff3fe3 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 13:48:44 -0500 Subject: [PATCH 078/118] closes #121 --- vignettes/An_example_with_simulated_data.Rmd | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/vignettes/An_example_with_simulated_data.Rmd b/vignettes/An_example_with_simulated_data.Rmd index 66010639..6362e1ff 100644 --- a/vignettes/An_example_with_simulated_data.Rmd +++ b/vignettes/An_example_with_simulated_data.Rmd @@ -209,6 +209,13 @@ sspm_model_fit <- sspm_model %>% sspm_model_fit ``` +It is possible to access the GAM fit object in order to look at it in more details and, for example, evaluate the goodness of fit. + +```{r} +gam_fit <- spm_get_fit(sspm_model_fit) +summary(gam_fit) +``` + 16. Plotting the object produces a actual vs predicted plot (with TEST/TRAIN data highlighted. ```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} @@ -220,6 +227,7 @@ plot(sspm_model_fit, train_test = TRUE, scales = "free") preds <- predict(sspm_model_fit) head(preds) ``` + We can also get the predictions for biomass by passing the biomass variable name. ```{r} biomass_preds <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis") From 70827ae107e99181d921cfd37b92babd06d79e4c Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 13:52:48 -0500 Subject: [PATCH 079/118] closes #124 --- .Rbuildignore | 1 + CODE_OF_CONDUCT.md | 126 +++++++++++++++++++++++++++++++++++++++++++++ README.Rmd | 7 +++ 3 files changed, 134 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/.Rbuildignore b/.Rbuildignore index 6a8b34b4..c345203f 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -14,3 +14,4 @@ man/figures/make_hex ^cran-comments\.md$ ^CRAN-SUBMISSION$ ^paper$ +^CODE_OF_CONDUCT\.md$ diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..d9feb3f6 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,126 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at eric.pedersen@concordia.ca. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion]. + +For answers to common questions about this code of conduct, see the FAQ at +. Translations are available at . + +[homepage]: https://www.contributor-covenant.org diff --git a/README.Rmd b/README.Rmd index 6bf9c7b4..72ea453d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -124,3 +124,10 @@ full bibliographic reference to include in your reference list Or see the [package citation page](https://pedersen-fisheries-lab.github.io/sspm/authors.html#citation). + +## Code of Conduct + +Please note that the sspm project is released with a [Contributor Code +of +Conduct]([https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).) +By contributing to this project, you agree to abide by its terms. From 02cdc9d0b58e1d1e5df581df9f025976be980694 Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 14:01:38 -0500 Subject: [PATCH 080/118] closes #123 --- R/spm_as_boundary.R | 3 ++- R/spm_as_dataset.R | 3 ++- R/spm_discretize.R | 3 ++- man/spm_as_boundary.Rd | 3 ++- man/spm_as_dataset.Rd | 3 ++- man/spm_discretize.Rd | 3 ++- 6 files changed, 12 insertions(+), 6 deletions(-) diff --git a/R/spm_as_boundary.R b/R/spm_as_boundary.R index 595b88de..e37a9dbe 100644 --- a/R/spm_as_boundary.R +++ b/R/spm_as_boundary.R @@ -1,6 +1,7 @@ #' Create a sspm_boundary object #' -#' Create a sspm_boundary object. +#' Create a sspm_boundary object. A boundary object serves as a basis to encode +#' the spatial extent of the model. #' #' @param boundaries **\[sf\]** The sf object to cast. #' @param boundary **\[character\]** The column that contains the possible diff --git a/R/spm_as_dataset.R b/R/spm_as_dataset.R index 5fae624a..dd6bcc6f 100644 --- a/R/spm_as_dataset.R +++ b/R/spm_as_dataset.R @@ -1,7 +1,8 @@ #' Create a `sspm_dataset` dataset structure #' #' This casts a `data.frame` or `sf` object into an object of class -#' [`sspm_dataset`][sspm_dataset-class]. +#' [`sspm_dataset`][sspm_dataset-class]. This object is the format the package +#' uses to manage and manipulate the modeling data. #' #' @param data **\[data.frame OR sf\]** The dataset. #' @param time **\[character\]** The column of `data` for the temporal diff --git a/R/spm_discretize.R b/R/spm_discretize.R index c5d807ff..7386e691 100644 --- a/R/spm_discretize.R +++ b/R/spm_discretize.R @@ -1,7 +1,8 @@ #' Discretize a `sspm` model object #' #' Discretize a [sspm][sspm-class] model object with a function from a -#' [discretization_method][discretization_method-class] object class. +#' [discretization_method][discretization_method-class] object class. This +#' function divides the boundary polygons into smaller patches. #' #' @param boundary_object **\[sspm\]** An object of class #' [sspm_boundary][sspm-class]. diff --git a/man/spm_as_boundary.Rd b/man/spm_as_boundary.Rd index fa06cb5e..78db44d3 100644 --- a/man/spm_as_boundary.Rd +++ b/man/spm_as_boundary.Rd @@ -74,7 +74,8 @@ An object of class \link[=sspm_boundary-class]{sspm_boundary} or \link[=sspm_discrete_boundary-class]{sspm_discrete_boundary}. } \description{ -Create a sspm_boundary object. +Create a sspm_boundary object. A boundary object serves as a basis to encode +the spatial extent of the model. } \examples{ sfa_boundaries diff --git a/man/spm_as_dataset.Rd b/man/spm_as_dataset.Rd index 1698ccb4..2ad10b67 100644 --- a/man/spm_as_dataset.Rd +++ b/man/spm_as_dataset.Rd @@ -100,7 +100,8 @@ An object of class \code{\link[=sspm_dataset-class]{sspm_dataset}}. } \description{ This casts a \code{data.frame} or \code{sf} object into an object of class -\code{\link[=sspm_dataset-class]{sspm_dataset}}. +\code{\link[=sspm_dataset-class]{sspm_dataset}}. This object is the format the package +uses to manage and manipulate the modeling data. } \examples{ data(borealis_simulated, package = "sspm") diff --git a/man/spm_discretize.Rd b/man/spm_discretize.Rd index 87ebd166..f708cc8b 100644 --- a/man/spm_discretize.Rd +++ b/man/spm_discretize.Rd @@ -45,7 +45,8 @@ and discretized \code{sspm} object given as input). } \description{ Discretize a \link[=sspm-class]{sspm} model object with a function from a -\link[=discretization_method-class]{discretization_method} object class. +\link[=discretization_method-class]{discretization_method} object class. This +function divides the boundary polygons into smaller patches. } \details{ Custom discretization functions can be written. The function must: From d791b279c61f5c9fa827b3b4f9ffaaaeb56837cf Mon Sep 17 00:00:00 2001 From: vlucet Date: Wed, 7 Dec 2022 14:01:38 -0500 Subject: [PATCH 081/118] closes #123 --- R/methods-show.R | 9 --------- R/spm_as_boundary.R | 3 ++- R/spm_as_dataset.R | 3 ++- R/spm_discretize.R | 3 ++- man/spm_as_boundary.Rd | 3 ++- man/spm_as_dataset.Rd | 3 ++- man/spm_discretize.Rd | 3 ++- 7 files changed, 12 insertions(+), 15 deletions(-) diff --git a/R/methods-show.R b/R/methods-show.R index 2f888e37..8b2e9d8c 100644 --- a/R/methods-show.R +++ b/R/methods-show.R @@ -101,7 +101,6 @@ setMethod("show", cli::cat_line() custom_h1("Model fit") cat_smoothed_data(object, prints = FALSE) - cat_spm_fit(object) cli::cat_line() } ) @@ -282,14 +281,6 @@ cat_smoothed_data <- function(object, prints = TRUE) { } -cat_spm_fit <- function(object) { - the_fit <- spm_get_fit(object) - the_fit_summary <- summary(the_fit) - cli::cat_bullet(" Fit summary : ", - bullet = "arrow_right") - show(the_fit_summary) -} - # ------------------------------------------------------------------------- pluralize_data_info <- function(object, diff --git a/R/spm_as_boundary.R b/R/spm_as_boundary.R index 595b88de..e37a9dbe 100644 --- a/R/spm_as_boundary.R +++ b/R/spm_as_boundary.R @@ -1,6 +1,7 @@ #' Create a sspm_boundary object #' -#' Create a sspm_boundary object. +#' Create a sspm_boundary object. A boundary object serves as a basis to encode +#' the spatial extent of the model. #' #' @param boundaries **\[sf\]** The sf object to cast. #' @param boundary **\[character\]** The column that contains the possible diff --git a/R/spm_as_dataset.R b/R/spm_as_dataset.R index 5fae624a..dd6bcc6f 100644 --- a/R/spm_as_dataset.R +++ b/R/spm_as_dataset.R @@ -1,7 +1,8 @@ #' Create a `sspm_dataset` dataset structure #' #' This casts a `data.frame` or `sf` object into an object of class -#' [`sspm_dataset`][sspm_dataset-class]. +#' [`sspm_dataset`][sspm_dataset-class]. This object is the format the package +#' uses to manage and manipulate the modeling data. #' #' @param data **\[data.frame OR sf\]** The dataset. #' @param time **\[character\]** The column of `data` for the temporal diff --git a/R/spm_discretize.R b/R/spm_discretize.R index c5d807ff..7386e691 100644 --- a/R/spm_discretize.R +++ b/R/spm_discretize.R @@ -1,7 +1,8 @@ #' Discretize a `sspm` model object #' #' Discretize a [sspm][sspm-class] model object with a function from a -#' [discretization_method][discretization_method-class] object class. +#' [discretization_method][discretization_method-class] object class. This +#' function divides the boundary polygons into smaller patches. #' #' @param boundary_object **\[sspm\]** An object of class #' [sspm_boundary][sspm-class]. diff --git a/man/spm_as_boundary.Rd b/man/spm_as_boundary.Rd index fa06cb5e..78db44d3 100644 --- a/man/spm_as_boundary.Rd +++ b/man/spm_as_boundary.Rd @@ -74,7 +74,8 @@ An object of class \link[=sspm_boundary-class]{sspm_boundary} or \link[=sspm_discrete_boundary-class]{sspm_discrete_boundary}. } \description{ -Create a sspm_boundary object. +Create a sspm_boundary object. A boundary object serves as a basis to encode +the spatial extent of the model. } \examples{ sfa_boundaries diff --git a/man/spm_as_dataset.Rd b/man/spm_as_dataset.Rd index 1698ccb4..2ad10b67 100644 --- a/man/spm_as_dataset.Rd +++ b/man/spm_as_dataset.Rd @@ -100,7 +100,8 @@ An object of class \code{\link[=sspm_dataset-class]{sspm_dataset}}. } \description{ This casts a \code{data.frame} or \code{sf} object into an object of class -\code{\link[=sspm_dataset-class]{sspm_dataset}}. +\code{\link[=sspm_dataset-class]{sspm_dataset}}. This object is the format the package +uses to manage and manipulate the modeling data. } \examples{ data(borealis_simulated, package = "sspm") diff --git a/man/spm_discretize.Rd b/man/spm_discretize.Rd index 87ebd166..f708cc8b 100644 --- a/man/spm_discretize.Rd +++ b/man/spm_discretize.Rd @@ -45,7 +45,8 @@ and discretized \code{sspm} object given as input). } \description{ Discretize a \link[=sspm-class]{sspm} model object with a function from a -\link[=discretization_method-class]{discretization_method} object class. +\link[=discretization_method-class]{discretization_method} object class. This +function divides the boundary polygons into smaller patches. } \details{ Custom discretization functions can be written. The function must: From ee1d2b38521a63163898281e91414b64e27570da Mon Sep 17 00:00:00 2001 From: vlucet Date: Thu, 8 Dec 2022 10:05:47 -0500 Subject: [PATCH 082/118] add RTriangle for cran --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 548e1003..c9facd50 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,7 +33,8 @@ Imports: dplyr, purrr, stringr, - rlang + rlang, + RTriangle Suggests: testthat (>= 3.0.0), covr, From d49cece59fa1ccb7b98401234cdcc9141b714f73 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:01:59 -0500 Subject: [PATCH 083/118] update renv --- renv.lock | 15 ++++----- renv/activate.R | 84 ++++++++++--------------------------------------- 2 files changed, 22 insertions(+), 77 deletions(-) diff --git a/renv.lock b/renv.lock index 17b7fae8..992471ce 100644 --- a/renv.lock +++ b/renv.lock @@ -1,6 +1,6 @@ { "R": { - "Version": "4.2.1", + "Version": "4.2.2", "Repositories": [ { "Name": "CRAN", @@ -192,13 +192,11 @@ }, "cli": { "Package": "cli", - "Version": "3.3.0", + "Version": "3.6.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "23abf173c2b783dcc43379ab9bba00ee", - "Requirements": [ - "glue" - ] + "Hash": "3177a5a16c243adc199ba33117bd9657", + "Requirements": [] }, "colorspace": { "Package": "colorspace", @@ -959,16 +957,15 @@ }, "testthat": { "Package": "testthat", - "Version": "3.1.4", + "Version": "3.1.6", "Source": "Repository", "Repository": "CRAN", - "Hash": "f76c2a02d0fdc24aa7a47ea34261a6e3", + "Hash": "7910146255835c66e9eb272fb215248d", "Requirements": [ "R6", "brio", "callr", "cli", - "crayon", "desc", "digest", "ellipsis", diff --git a/renv/activate.R b/renv/activate.R index fd15fa87..72c0818a 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -185,80 +185,43 @@ local({ if (fixup) mode <- "w+b" - args <- list( + utils::download.file( url = url, destfile = destfile, mode = mode, quiet = TRUE ) - if ("headers" %in% names(formals(utils::download.file))) - args$headers <- renv_bootstrap_download_custom_headers(url) - - do.call(utils::download.file, args) - - } - - renv_bootstrap_download_custom_headers <- function(url) { - - headers <- getOption("renv.download.headers") - if (is.null(headers)) - return(character()) - - if (!is.function(headers)) - stopf("'renv.download.headers' is not a function") - - headers <- headers(url) - if (length(headers) == 0L) - return(character()) - - if (is.list(headers)) - headers <- unlist(headers, recursive = FALSE, use.names = TRUE) - - ok <- - is.character(headers) && - is.character(names(headers)) && - all(nzchar(names(headers))) - - if (!ok) - stop("invocation of 'renv.download.headers' did not return a named character vector") - - headers - } renv_bootstrap_download_cran_latest <- function(version) { spec <- renv_bootstrap_download_cran_latest_find(version) - type <- spec$type - repos <- spec$repos message("* Downloading renv ", version, " ... ", appendLF = FALSE) - baseurl <- utils::contrib.url(repos = repos, type = type) - ext <- if (identical(type, "source")) - ".tar.gz" - else if (Sys.info()[["sysname"]] == "Windows") - ".zip" - else - ".tgz" - name <- sprintf("renv_%s%s", version, ext) - url <- paste(baseurl, name, sep = "/") + type <- spec$type + repos <- spec$repos - destfile <- file.path(tempdir(), name) - status <- tryCatch( - renv_bootstrap_download_impl(url, destfile), + info <- tryCatch( + utils::download.packages( + pkgs = "renv", + destdir = tempdir(), + repos = repos, + type = type, + quiet = TRUE + ), condition = identity ) - if (inherits(status, "condition")) { + if (inherits(info, "condition")) { message("FAILED") return(FALSE) } # report success and return message("OK (downloaded ", type, ")") - destfile + info[1, 2] } @@ -715,7 +678,7 @@ local({ return(profile) # check for a profile file (nothing to do if it doesn't exist) - path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project) + path <- renv_bootstrap_paths_renv("profile", profile = FALSE) if (!file.exists(path)) return(NULL) @@ -842,23 +805,9 @@ local({ renv_json_read <- function(file = NULL, text = NULL) { - # if jsonlite is loaded, use that instead - if ("jsonlite" %in% loadedNamespaces()) - renv_json_read_jsonlite(file, text) - else - renv_json_read_default(file, text) - - } - - renv_json_read_jsonlite <- function(file = NULL, text = NULL) { text <- paste(text %||% read(file), collapse = "\n") - jsonlite::fromJSON(txt = text, simplifyVector = FALSE) - } - - renv_json_read_default <- function(file = NULL, text = NULL) { # find strings in the JSON - text <- paste(text %||% read(file), collapse = "\n") pattern <- '["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]' locs <- gregexpr(pattern, text, perl = TRUE)[[1]] @@ -889,9 +838,8 @@ local({ # transform the JSON into something the R parser understands transformed <- replaced - transformed <- gsub("{}", "`names<-`(list(), character())", transformed, fixed = TRUE) - transformed <- gsub("[[{]", "list(", transformed, perl = TRUE) - transformed <- gsub("[]}]", ")", transformed, perl = TRUE) + transformed <- gsub("[[{]", "list(", transformed) + transformed <- gsub("[]}]", ")", transformed) transformed <- gsub(":", "=", transformed, fixed = TRUE) text <- paste(transformed, collapse = "\n") From 23ade06d5840688aea76b8e9d330274aa0356c40 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:02:25 -0500 Subject: [PATCH 084/118] remove useless code --- R/methods-show.R | 6 ------ 1 file changed, 6 deletions(-) diff --git a/R/methods-show.R b/R/methods-show.R index 8b2e9d8c..fbb94c46 100644 --- a/R/methods-show.R +++ b/R/methods-show.R @@ -7,12 +7,6 @@ custom_h1 <- function(text) { cli::symbol$figure_dash, cli::symbol$figure_dash) } -custom_h3 <- function(text) { - cli::cat_line( - cli::symbol$figure_dash, - " ", cli::style_bold(text), " ") -} - setMethod("show", "sspm_boundary", function(object) { From 9c54b6c6a52ceef094407f8d54a67ddcfe223b5e Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:03:48 -0500 Subject: [PATCH 085/118] update as dataset to work best with polygons --- R/spm_as_dataset.R | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/R/spm_as_dataset.R b/R/spm_as_dataset.R index dd6bcc6f..2ff58bfd 100644 --- a/R/spm_as_dataset.R +++ b/R/spm_as_dataset.R @@ -164,12 +164,13 @@ setMethod(f = "spm_as_dataset", patches <- data %>% dplyr::select("geometry") %>% dplyr::distinct() %>% + dplyr::mutate(patch_id = paste("P", 1:dplyr::n(), sep = "")) %>% dplyr::mutate(patch_id = - factor(paste("P", 1:dplyr::n(), sep = ""), - levels = paste0("P", 1:length(unique(.data$patch_id))))) %>% + factor(patch_id, levels = + paste0("P", 1:length(unique(.data$patch_id))))) %>% dplyr::mutate(boundary_col = "B1") - # Fuse all patchaes in a single boundary object + # Fuse all patches in a single boundary object boundary_data <- patches %>% sf::st_union() %>% sf::st_as_sf() %>% From 7e0f997ade3f1c2ed2020d5845b2fc502cf6c12c Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:04:18 -0500 Subject: [PATCH 086/118] move code for checking if already discretized --- R/spm_discretize.R | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/R/spm_discretize.R b/R/spm_discretize.R index 7386e691..0cccbb59 100644 --- a/R/spm_discretize.R +++ b/R/spm_discretize.R @@ -130,6 +130,11 @@ setMethod(f = "spm_discretize", method = "discretization_method"), function(boundary_object, method, with, ...) { + if (checkmate::test_class(boundary_object, + "sspm_discrete_boundary")) { + stop(" Boundary is already discretized") + } + if (checkmate::test_class(with, "sspm_dataset")){ with <- spm_data(with) } else if (!is.null(with)) { @@ -170,14 +175,3 @@ setMethod(f = "spm_discretize", return(new_sspm_discrete_boundary) } ) - -# RE-discretization not allowed for now -#' @rdname spm_discretize -#' @export -setMethod(f = "spm_discretize", - signature(boundary_object = "sspm_discrete_boundary"), - function(boundary_object, method, with, ...) { - - cli::cli_alert_danger(paste0(" Boundary is already discretized")) - } -) From 319dabfaaec8811998bd902fbdbbc8542d02050c Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:09:52 -0500 Subject: [PATCH 087/118] re document --- R/spm_split.R | 2 +- man/spm_discretize.Rd | 3 --- man/spm_split.Rd | 2 +- 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/R/spm_split.R b/R/spm_split.R index d36aca37..a26d7aee 100644 --- a/R/spm_split.R +++ b/R/spm_split.R @@ -1,6 +1,6 @@ #' Split data in test and train sets #' -#' Split data before fitting spm (WIP). +#' Split data before fitting spm. #' #' @param sspm_object **\[sspm\]** An object of class [sspm][sspm-class]. #' @param ... **\[expression\]** Expression to evaluate to split data. diff --git a/man/spm_discretize.Rd b/man/spm_discretize.Rd index f708cc8b..712a9df6 100644 --- a/man/spm_discretize.Rd +++ b/man/spm_discretize.Rd @@ -7,7 +7,6 @@ \alias{spm_discretize,sspm_boundary,character,ANY-method} \alias{spm_discretize,sspm_boundary,function,ANY-method} \alias{spm_discretize,sspm_boundary,discretization_method,ANY-method} -\alias{spm_discretize,sspm_discrete_boundary,ANY,ANY-method} \title{Discretize a \code{sspm} model object} \usage{ spm_discretize(boundary_object, method = "tesselate_voronoi", with = NULL, ...) @@ -21,8 +20,6 @@ spm_discretize(boundary_object, method = "tesselate_voronoi", with = NULL, ...) \S4method{spm_discretize}{sspm_boundary,`function`,ANY}(boundary_object, method = "tesselate_voronoi", with = NULL, ...) \S4method{spm_discretize}{sspm_boundary,discretization_method,ANY}(boundary_object, method = "tesselate_voronoi", with = NULL, ...) - -\S4method{spm_discretize}{sspm_discrete_boundary,ANY,ANY}(boundary_object, method = "tesselate_voronoi", with = NULL, ...) } \arguments{ \item{boundary_object}{\strong{[sspm]} An object of class diff --git a/man/spm_split.Rd b/man/spm_split.Rd index 1f610911..3391e936 100644 --- a/man/spm_split.Rd +++ b/man/spm_split.Rd @@ -18,7 +18,7 @@ spm_split(sspm_object, ...) The updated sspm object. } \description{ -Split data before fitting spm (WIP). +Split data before fitting spm. } \examples{ \dontrun{ From c4f83c485cea46931ed6d49e2fa8b5030c87e6d6 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 15:38:24 -0500 Subject: [PATCH 088/118] move test cov to 80% --- tests/testthat/helper-tests.R | 34 +++++++-- tests/testthat/test-accessors.R | 6 ++ tests/testthat/test-as-methods.R | 11 +++ tests/testthat/test-discretize.R | 58 ++++++++++++++- tests/testthat/test-extracts.R | 0 tests/testthat/test-fit.R | 2 +- tests/testthat/test-help-funcs.R | 14 ++++ tests/testthat/test-plot.R | 2 - tests/testthat/test-replacers.R | 17 +++++ tests/testthat/test-show.R | 3 +- tests/testthat/test-smooths.R | 2 + tests/testthat/test-sspm.R | 24 ++++++- tests/testthat/test-z_integration.R | 107 ++++++++++++++++++++++++++++ 13 files changed, 265 insertions(+), 15 deletions(-) create mode 100644 tests/testthat/test-extracts.R create mode 100644 tests/testthat/test-z_integration.R diff --git a/tests/testthat/helper-tests.R b/tests/testthat/helper-tests.R index 49e80a77..92a709cb 100644 --- a/tests/testthat/helper-tests.R +++ b/tests/testthat/helper-tests.R @@ -14,12 +14,26 @@ data(borealis_simulated, package = "sspm") data(predator_simulated, package = "sspm") data(sfa_boundaries, package = "sspm") data(catch_simulated, package = "sspm") + sfa_boundaries <- sfa_boundaries %>% dplyr::mutate(area = sf::st_area(sfa_boundaries)) + borealis_patches <- sspm:::borealis_patches borealis_points <- sspm:::borealis_points -borealis_spatial <- sspm:::borealis_spatial -predator_spatial <- sspm:::predator_spatial + +borealis_spatial <- sspm:::borealis_spatial %>% + mutate(year = as.numeric(as.character(year_f))) + +predator_spatial <- sspm:::predator_spatial %>% + mutate(year = as.numeric(as.character(year_f))) + +borealis_spatial_joined <- borealis_spatial %>% + st_join(select(borealis_patches, "patch_id")) %>% + dplyr::mutate("row_ID" = 1:nrow(borealis_spatial)) + +predator_spatial_joined <- predator_spatial %>% + st_join(select(borealis_patches, "patch_id")) %>% + dplyr::mutate("row_ID" = 1:nrow(predator_spatial)) # Create objects ---------------------------------------------------------- @@ -40,12 +54,13 @@ boundary_discrete <- new("sspm_discrete_boundary", method = discret_method, patches = borealis_patches, points = borealis_points, - boundary_area = "area") + boundary_area = "area", + patches_area = "patch_area") # Base objects biomass_dataset <- new("sspm_dataset", data = borealis_spatial, - time = "year_f", + time = "year", density = "weight_per_km2", uniqueID = "uniqueID", coords = c('lon_dec', 'lat_dec')) @@ -80,19 +95,22 @@ sspm_formula <- new("sspm_formula", # Smoothed objects biomass_dataset_smoothed <- new("sspm_dataset", + name = "biomass_test", data = borealis_spatial, density = "weight_per_km2", boundaries = boundary_discrete, - time = "year_f", + time = "year", uniqueID = "uniqueID", coords = c('lon_dec', 'lat_dec'), formulas = list(sspm_formula), - smoothed_data = borealis_spatial, + smoothed_vars = "weight_per_km2", + smoothed_data = borealis_spatial_joined, smoothed_fit = list(mgcv::gam(mtcars$mpg~mtcars$cyl, family = gaussian)), is_mapped = TRUE) predator_dataset_smoothed <- new("sspm_dataset", + name = "predator_test", data = predator_spatial, density = "weight_per_km2", boundaries = boundary_discrete, @@ -100,10 +118,12 @@ predator_dataset_smoothed <- new("sspm_dataset", uniqueID = "uniqueID", coords = c('lon_dec', 'lat_dec'), formulas = list(sspm_formula), - smoothed_data = predator_spatial, + smoothed_vars = "weight_per_km2", + smoothed_data = predator_spatial_joined, smoothed_fit = list(mgcv::gam(mtcars$mpg~mtcars$cyl, family = gaussian)), is_mapped = TRUE) + # ------------------------------------------------------------------------- all_data <- list(biomass = biomass_dataset_smoothed, diff --git a/tests/testthat/test-accessors.R b/tests/testthat/test-accessors.R index 838f49a0..30933e49 100644 --- a/tests/testthat/test-accessors.R +++ b/tests/testthat/test-accessors.R @@ -12,6 +12,8 @@ test_that("Accessors work as expected on `sspm_boundary` (discrete or not)", { expect_equal(spm_boundary_area(boundary), "area") + expect_equal(spm_patches_area(boundary_discrete), "patch_area") + expect_equal(spm_points(boundary_discrete), borealis_points) expect_equal(spm_patches(boundary_discrete), borealis_patches) @@ -58,6 +60,10 @@ test_that("Accessors work as expected on `sspm_dataset` (smoothed or not)", { expect_list(spm_formulas(biomass_dataset_smoothed)) expect_length(spm_formulas(biomass_dataset_smoothed), 1) + expect_null(spm_biomass_vars(biomass_dataset)) + expect_equal(spm_biomass_vars(catch_dataset), "catch") + expect_equal(spm_density_vars(biomass_dataset), "weight_per_km2") + }) test_that("Accessors work as expected on `sspm`", { diff --git a/tests/testthat/test-as-methods.R b/tests/testthat/test-as-methods.R index 15109fad..867507b5 100644 --- a/tests/testthat/test-as-methods.R +++ b/tests/testthat/test-as-methods.R @@ -102,4 +102,15 @@ test_that("sspm dataset is casted correctly", { uniqueID = "uniqueID") }, "sspm_dataset") + test_polygons <- borealis_patches %>% + mutate(year_f = as.factor("2000"), + uniqueID = 1:nrow(borealis_patches)) + + expect_class({ + spm_as_dataset(data = test_polygons, + time = "year_f", + name = "Biomass", + uniqueID = "uniqueID") + }, "sspm_dataset") + }) diff --git a/tests/testthat/test-discretize.R b/tests/testthat/test-discretize.R index 72623f8e..0d86312e 100644 --- a/tests/testthat/test-discretize.R +++ b/tests/testthat/test-discretize.R @@ -9,17 +9,64 @@ test_that("Discretization work as expected", { expect_class({discretized}, "sspm_discrete_boundary") + expect_class({boundary %>% + spm_discretize(method = "tesselate_voronoi", + with = biomass_dataset, + stratify = FALSE, + nb_samples = 30)}, + "sspm_discrete_boundary") + + expect_class({boundary %>% + spm_discretize(method = "tesselate_voronoi", + sample_surface = TRUE, + nb_samples = 30)}, + "sspm_discrete_boundary") + + expect_class({boundary %>% + spm_discretize(method = "tesselate_voronoi", + with = biomass_dataset, + sample_points = FALSE)}, + "sspm_discrete_boundary") + expect_class({boundary %>% spm_discretize(method = "tesselate_voronoi", with = biomass_dataset, nb_samples = c(`4` = 5, `5` = 10, `6` = 7, `7` = 2))}, "sspm_discrete_boundary") + expect_class({boundary %>% + spm_discretize(method = tesselate_voronoi, + with = biomass_dataset, + nb_samples = c(`4` = 5, `5` = 10, `6` = 7, `7` = 2))}, + "sspm_discrete_boundary") + + # ---------------------------------------------------------------------- + expect_class({boundary %>% spm_discretize(method = "triangulate_delaunay", with = biomass_dataset)}, "sspm_discrete_boundary") + expect_class({boundary %>% + spm_discretize(method = "triangulate_delaunay", + with = biomass_dataset, + sample_surface = TRUE, + nb_samples = 10)}, + "sspm_discrete_boundary") + + expect_class({boundary %>% + spm_discretize(method = "triangulate_delaunay", + with = biomass_dataset, + sample_points = TRUE, + nb_samples = 10)}, + "sspm_discrete_boundary") + + expect_class({boundary %>% + spm_discretize(method = "triangulate_delaunay")}, + "sspm_discrete_boundary") + + # ---------------------------------------------------------------------- + expect_names(names(spm_boundaries(discretized)), must.include = c("sfa", "area")) expect_names(names(spm_patches(discretized)), @@ -29,7 +76,7 @@ test_that("Discretization work as expected", { expect_equal(dim(spm_patches(discretized))[2], 4) expect_equal(dim(spm_points(discretized))[1], 120) - expect_equal(dim(spm_points(discretized))[2], 10) + expect_equal(dim(spm_points(discretized))[2], 11) # ---------------------------------------------------------------------- @@ -44,4 +91,13 @@ test_that("Discretization work as expected", { with = biomass_dataset)}, "Method 'wrong_method' is not part of the supported methods.") + expect_error({boundary %>% + spm_discretize(with = biomass_dataset)}, + "method argument missing.") + + expect_error({discretized %>% + spm_discretize(method = "tesselate_voronoi", + with = biomass_dataset)}, + "Boundary is already discretized") + }) diff --git a/tests/testthat/test-extracts.R b/tests/testthat/test-extracts.R new file mode 100644 index 00000000..e69de29b diff --git a/tests/testthat/test-fit.R b/tests/testthat/test-fit.R index 6e0ce729..1ed3bba6 100644 --- a/tests/testthat/test-fit.R +++ b/tests/testthat/test-fit.R @@ -12,5 +12,5 @@ test_that("Fitting works as expected", { family=gaussian) intercept <- fit@smoothed_fit$weight_per_km2$coefficients[[1]] - expect_equal(intercept, 7.84565, tolerance = 1e-5) + expect_equal(intercept, 7.855774, tolerance = 1e-5) }) diff --git a/tests/testthat/test-help-funcs.R b/tests/testthat/test-help-funcs.R index 583afe9e..5d33fee8 100644 --- a/tests/testthat/test-help-funcs.R +++ b/tests/testthat/test-help-funcs.R @@ -19,6 +19,20 @@ test_that("Aggregation choices are retrieved", { }) +test_that("Aggregation levels are retrieved", { + + choices <- spm_aggregation_levels_choices() + expect_equal(choices, c("patch", "boundary")) + +}) + +test_that("Aggregation types are retrieved", { + + choices <- spm_aggregation_types_choices() + expect_equal(choices, c("data", "smoothed")) + +}) + test_that("Methods are dispatched correctly", { expect_equal(dispatch_method("tesselate_voronoi"), tesselate_voronoi) diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R index a7e23a37..53261d32 100644 --- a/tests/testthat/test-plot.R +++ b/tests/testthat/test-plot.R @@ -3,6 +3,4 @@ test_that("Plotting works as expected", { skip("TODO") - # TODO - }) diff --git a/tests/testthat/test-replacers.R b/tests/testthat/test-replacers.R index 8d348f60..096f60a8 100644 --- a/tests/testthat/test-replacers.R +++ b/tests/testthat/test-replacers.R @@ -36,6 +36,11 @@ test_that("Replacers work as expected", { spm_patches(boundary_discrete) }, sfa_boundaries) + expect_equal({ + spm_patches_area(boundary_discrete) <- "new_col_3" + spm_patches_area(boundary_discrete) + }, "new_col_3") + # SSPM expect_class({ @@ -144,4 +149,16 @@ test_that("Replacers work as expected", { spm_formulas(biomass_dataset_smoothed) }, list(sspm_formula)) + expect_equal({ + spm_biomass_vars(catch_dataset) <- c("catch", "catch_replaced") + spm_biomass_vars(catch_dataset) + }, c("catch", "catch_replaced")) + + expect_equal({ + spm_density_vars(biomass_dataset) <- c("weight_per_km2", + "weight_per_km2_replaced") + spm_density_vars(biomass_dataset) + }, c("weight_per_km2", + "weight_per_km2_replaced")) + }) diff --git a/tests/testthat/test-show.R b/tests/testthat/test-show.R index 7c01f6c1..0412c08d 100644 --- a/tests/testthat/test-show.R +++ b/tests/testthat/test-show.R @@ -1,10 +1,11 @@ # Test the show methods -test_that("Shpw methods print out fine", { +test_that("Show methods print out fine", { expect_output(show(boundary)) expect_output(show(boundary_discrete)) expect_output(show(biomass_dataset)) + expect_output(show(biomass_dataset_smoothed)) expect_output(show(sspm_formula)) expect_output(show(discret_method)) expect_output(show(sspm_model)) diff --git a/tests/testthat/test-smooths.R b/tests/testthat/test-smooths.R index d0b08b55..c866dfe7 100644 --- a/tests/testthat/test-smooths.R +++ b/tests/testthat/test-smooths.R @@ -67,6 +67,8 @@ test_that("Main smooth functions work as expected", { expect_equal(dim(res_space_time$vars$pen_mat_time), c(24, 24)) expect_equal(dim(res_space_time$vars$pen_mat_space), c(69, 69)) + # res_lag <- smooth_lag(dataset = "Biomass", sspm_object = sspm_discrete_mapped) + }) test_that("ICAR function works as expected", { diff --git a/tests/testthat/test-sspm.R b/tests/testthat/test-sspm.R index f35d7dec..e0948436 100644 --- a/tests/testthat/test-sspm.R +++ b/tests/testthat/test-sspm.R @@ -1,8 +1,26 @@ -# Test sspm function +# Test sspm functions test_that("sspm object are created correcly", { - skip("TODO") - # TODO + sspm_model <- sspm(biomass = biomass_dataset_smoothed, + predictors = predator_dataset_smoothed) + expect_class(sspm_model, "sspm") + + expect_class({ + sspm(biomass = biomass_dataset_smoothed) + }, "sspm") + + sspm_model_split <- sspm_model %>% + spm_split(year %in% c(1990:2017)) + expect_class(sspm_model_split, "sspm") + expect_true(is_split(sspm_model_split)) + + + sspm_model_lag <- sspm_model_split %>% + spm_lag(vars = c("weight_per_km2_biomass_test", + "weight_per_km2_predator_test"), + n = 1) + + expect_class(sspm_model_lag, "sspm") }) diff --git a/tests/testthat/test-z_integration.R b/tests/testthat/test-z_integration.R new file mode 100644 index 00000000..8059fda3 --- /dev/null +++ b/tests/testthat/test-z_integration.R @@ -0,0 +1,107 @@ +# Test integration (taken from vignette) + +test_that("Worklow works as expected", { + skip_on_cran() + + bounds <- spm_as_boundary(boundaries = sfa_boundaries, + boundary = "sfa") + + biomass_dataset <- + spm_as_dataset(borealis_simulated, name = "borealis", + density = "weight_per_km2", + time = "year_f", + coords = c('lon_dec','lat_dec'), + uniqueID = "uniqueID") + + predator_dataset <- + spm_as_dataset(predator_simulated, name = "all_predators", + density = "weight_per_km2", + time = "year_f", + coords = c("lon_dec", "lat_dec"), + uniqueID = "uniqueID") + + bounds_voronoi <- bounds %>% + spm_discretize(method = "tesselate_voronoi", + with = biomass_dataset, + nb_samples = 30) + + bounds_delaunay <- bounds %>% + spm_discretize(method = "triangulate_delaunay", a = 1, q = 30) + + biomass_smooth <- biomass_dataset %>% + spm_smooth(weight_per_km2 ~ sfa + smooth_time(by = sfa) + + smooth_space() + + smooth_space_time(), + boundaries = bounds_voronoi, + family=tw)%>% + spm_smooth(temp_at_bottom ~ smooth_time(by=sfa, xt = NULL) + + smooth_space() + + smooth_space_time(xt = NULL), + family=gaussian) + + predator_smooth <- predator_dataset %>% + spm_smooth(weight_per_km2 ~ smooth_time() + smooth_space(), + boundaries = bounds_voronoi, + drop.unused.levels = F, family=tw, method= "fREML") + + catch_dataset <- + spm_as_dataset(catch_simulated, name = "catch_data", + biomass = "catch", + time = "year_f", + uniqueID = "uniqueID", + coords = c("lon_dec", "lat_dec")) + + biomass_smooth_w_catch <- + spm_aggregate_catch(biomass = biomass_smooth, + catch = catch_dataset, + biomass_variable = "weight_per_km2", + catch_variable = "catch", + fill = mean) + + sspm_model <- sspm(biomass = biomass_smooth_w_catch, + predictors = predator_smooth) + + sspm_model <- sspm_model %>% + spm_split(year_f %in% c(1990:2017)) + + sspm_model <- sspm_model %>% + spm_lag(vars = c("weight_per_km2_borealis", + "weight_per_km2_all_predators"), + n = 1) + + sspm_model_fit <- sspm_model %>% + spm(log_productivity ~ sfa + + weight_per_km2_all_predators_lag_1 + + smooth_space(by = weight_per_km2_borealis_lag_1) + + smooth_space(), + family = mgcv::scat) + + gam_fit <- spm_get_fit(sspm_model_fit) + + preds <- predict(sspm_model_fit) + + biomass_preds <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis") + + biomass_one_step <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis", + next_ts = TRUE) + + ## Plots + + plot(bounds) + plot(bounds_voronoi) + plot(bounds_delaunay) + plot(biomass_smooth, var = "weight_per_km2", log = FALSE, interval = T) + plot(biomass_smooth, var = "weight_per_km2", use_sf = TRUE) + plot(sspm_model_fit, train_test = TRUE, scales = "free") + plot(sspm_model_fit, log = T, scales = 'free') + plot(sspm_model_fit, log = T, use_sf = TRUE) + plot(sspm_model_fit, biomass = "weight_per_km2_borealis", scales = "free") + plot(sspm_model_fit, biomass = "weight_per_km2_borealis", use_sf = TRUE) + plot(sspm_model_fit, biomass = "weight_per_km2_borealis", + next_ts = TRUE, aggregate = TRUE, scales = "free", + smoothed_biomass = TRUE, interval = T) + + ## Closes with an expectation + expect_data_frame(biomass_one_step) + +}) From eb936c69c943a1618321f8db1aadab0587ecf05b Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 16:06:06 -0500 Subject: [PATCH 089/118] delete trajectory --- R/plot_trajectory.R | 152 ----------------------------------------- man/plot_trajectory.Rd | 40 ----------- 2 files changed, 192 deletions(-) delete mode 100644 R/plot_trajectory.R delete mode 100644 man/plot_trajectory.Rd diff --git a/R/plot_trajectory.R b/R/plot_trajectory.R deleted file mode 100644 index ca6a880b..00000000 --- a/R/plot_trajectory.R +++ /dev/null @@ -1,152 +0,0 @@ -#' Plot trajectory of exploitation rate with biomass -#' -#' Provides a trajectory plot for exploitation rate (ER), defined as the ratio -#' between catch of a given year and fishable biomass from the previous year, -#' and that fishable biomass. -#' -#' @inheritParams spm_aggregate -#' @param dataset **\[sspm_dataset\]** Corresponding biomass dataset. -#' @param biomass **\[character\]** Biomass variable for plotting. -#' @param catch **\[character\]** Catch variable for plotting -#' @param dataset_biomass **\[character\]** Biomass variable for plotting in -#' the biomass dataset if the variable name is different there. -#' -#' @return -#' A ggplot2 plot object. -#' -#' @examples -#' \dontrun{ -#' plot_trajectory(sspm_fit, borealis_dataset_fitted, -#' "weight_per_km2", "catch") -#' } -#' -#' @export -#' @rdname plot_trajectory -setGeneric(name = "plot_trajectory", - def = function(sspm_object, - dataset, - biomass, - catch, - ...){ - - standardGeneric("plot_trajectory") - } -) - -# Methods ----------------------------------------------------------------- -#' @export -#' @rdname plot_trajectory -setMethod(f = "plot_trajectory", - signature(sspm_object = "sspm_fit", - dataset = "sspm_dataset", - biomass = "character", - catch = "character"), - function(sspm_object, - dataset, - biomass, - catch, - dataset_biomass = NULL){ - - # Get smoothed data - smoothed_data <- spm_smoothed_data(sspm_object) - bounds <- spm_boundaries(sspm_object) - boundary <- spm_boundary(bounds) - time <- spm_time(sspm_object) - patches_area <- spm_patches_area(bounds) - - if (is.null(dataset_biomass)) dataset_biomass <- biomass - - # TODO assert column biomass, catch - biomass_actual <- sspm_object %>% - spm_smoothed_data() %>% - dplyr::mutate(biomass = .data[[dataset_biomass]] * - .data[[patches_area]]) %>% - dplyr::group_by(.data[[time]], .data[[boundary]]) %>% - dplyr::summarise(biomass = sum(.data$biomass), - catch = sum(.data[[catch]])) %>% - sf::st_drop_geometry() %>% - dplyr::ungroup() - - biomass_actual <- biomass_actual %>% - dplyr::group_by(.data[[boundary]]) %>% - dplyr::mutate(catch = as.numeric(.data[[catch]]), - biomass = as.numeric(.data$biomass), - biomass_w_catch = .data$biomass + .data[[catch]]) %>% - dplyr::mutate(year_lag = - dplyr::lag(.data[[time]], n = 1), - biomass_lag = - dplyr::lag(.data$biomass, n = 1), - biomass_w_catch_lag = - dplyr::lag(.data$biomass_w_catch, n = 1), - catch_lag = - dplyr::lag(.data[[catch]], n = 1)) %>% - dplyr::mutate(ER = .data[[catch]]/.data$biomass_lag) %>% - dplyr::mutate(color = "Actual") - - # ----------------------------------------------------------- - - biomass_preds <- - predict(sspm_object, biomass = biomass, aggregate = TRUE, - interval = FALSE, next_ts = FALSE) %>% - sf::st_drop_geometry() - biomass_preds_next_ts <- - predict(sspm_object, biomass = biomass, aggregate = TRUE, - interval = FALSE, next_ts = TRUE) %>% - sf::st_drop_geometry() - - all_preds <- biomass_preds %>% - dplyr::bind_rows(biomass_preds_next_ts) %>% - dplyr::ungroup() - - catch_dat <- smoothed_data %>% - sf::st_drop_geometry() %>% - dplyr::select(.data[[boundary]], .data[[time]], - .data[[catch]]) %>% - dplyr::group_by(.data[[boundary]], .data[[time]]) %>% - dplyr::summarise(catch = sum(.data[[catch]])) %>% - dplyr::ungroup() - - all_preds_w_catch <- all_preds %>% - dplyr::left_join(catch_dat, by = c("sfa", "year_f")) %>% - dplyr::group_by(.data[[boundary]]) %>% - dplyr::mutate(catch = as.numeric(.data[[catch]]), - biomass = as.numeric(.data$biomass), - biomass_w_catch = .data$biomass + .data[[catch]]) %>% - dplyr::mutate(year_lag = - dplyr::lag(.data[[time]], n = 1), - biomass_lag = - dplyr::lag(.data$biomass, n = 1), - biomass_w_catch_lag = - dplyr::lag(.data$biomass_w_catch, n = 1), - catch_lag = - dplyr::lag(.data[[catch]], n = 1)) %>% - dplyr::mutate(ER = .data[[catch]]/.data$biomass_lag) %>% - dplyr::mutate(color = "Predictions") - - # stopifnot(all((all_data$biomass_w_catch_lag > all_data$catch), na.rm = T)) - # stopifnot(all((all_data$biomass_w_catch_lag > all_data$catch), na.rm = T)) - - all_data <- all_preds_w_catch %>% - dplyr::bind_rows(biomass_actual) - - all_data %>% - dplyr::filter(.data$ER > 0) %>% - dplyr::mutate(yr_label = paste0(.data$year_lag, " - ", .data[[time]])) %>% - # filter(year_f >= 2005) %>% - # filter(year_f <= 2014) %>% - ggplot2::ggplot() + - ggplot2::geom_point(ggplot2::aes(x = .data$biomass_lag, y = .data$ER, - color = .data$color)) + - ggplot2::geom_path(ggplot2::aes(x = .data$biomass_lag, y = .data$ER, - color = .data$color)) + - ggplot2::facet_wrap(~sfa, scales = "free") + - ggplot2::scale_color_manual(values = c("Actual" = "black", - "Predictions" = "red")) + - ggplot2::theme_light() + - ggplot2::labs(color = "Type") + - ggplot2::geom_text(ggplot2::aes(x = .data$biomass_lag, y = .data$ER, - color = .data$color, label = .data$yr_label), - hjust = 0, vjust = 0) - - } -) diff --git a/man/plot_trajectory.Rd b/man/plot_trajectory.Rd deleted file mode 100644 index 6303f692..00000000 --- a/man/plot_trajectory.Rd +++ /dev/null @@ -1,40 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plot_trajectory.R -\name{plot_trajectory} -\alias{plot_trajectory} -\alias{plot_trajectory,sspm_fit,sspm_dataset,character,character-method} -\title{Plot trajectory of exploitation rate with biomass} -\usage{ -plot_trajectory(sspm_object, dataset, biomass, catch, ...) - -\S4method{plot_trajectory}{sspm_fit,sspm_dataset,character,character}(sspm_object, dataset, biomass, catch, dataset_biomass = NULL) -} -\arguments{ -\item{sspm_object}{\strong{[sspm_dataset or sspm_fit]} The dataset object.} - -\item{dataset}{\strong{[sspm_dataset]} Corresponding biomass dataset.} - -\item{biomass}{\strong{[character]} Biomass variable for plotting.} - -\item{catch}{\strong{[character]} Catch variable for plotting} - -\item{...}{More arguments passed onto \code{fun}} - -\item{dataset_biomass}{\strong{[character]} Biomass variable for plotting in -the biomass dataset if the variable name is different there.} -} -\value{ -A ggplot2 plot object. -} -\description{ -Provides a trajectory plot for exploitation rate (ER), defined as the ratio -between catch of a given year and fishable biomass from the previous year, -and that fishable biomass. -} -\examples{ -\dontrun{ -plot_trajectory(sspm_fit, borealis_dataset_fitted, - "weight_per_km2", "catch") -} - -} From 000ee7e6821c951f74b0d30169896ef36c2d69b5 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 16:12:58 -0500 Subject: [PATCH 090/118] remove old deprecated tests --- tests/testthat/test-map-dataset.R | 84 ------------------------------- tests/testthat/test-map-formula.R | 42 ---------------- 2 files changed, 126 deletions(-) delete mode 100644 tests/testthat/test-map-dataset.R delete mode 100644 tests/testthat/test-map-formula.R diff --git a/tests/testthat/test-map-dataset.R b/tests/testthat/test-map-dataset.R deleted file mode 100644 index b9a9d40c..00000000 --- a/tests/testthat/test-map-dataset.R +++ /dev/null @@ -1,84 +0,0 @@ -# Test of map_dataset - -test_that("Mapping of dataset works as expected", { - skip("Deprecated") - - expect_error({ - sspm_discrete %>% map_dataset(predator_simulated, name = "pred_data", time_column = "year", - uniqueID = "uniqueID", coords = c("lat_dec")) - }, "`coords` must be of length 2") - - # Success cases - mapped <- sspm_discrete %>% - map_dataset(predator_simulated, name = "pred_data", time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - expect_length(spm_datasets(mapped), 2) - - mapped_from_df <- sspm_discrete %>% - map_dataset(predator_simulated, name = "pred_data", time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - expect_length(spm_datasets(mapped_from_df), 2) - - # Test list - # Error cases - dataset_list <- list(predator_simulated, predator_simulated) - expect_error({ - sspm_discrete %>% - map_dataset(dataset_list, name = "pred_data", time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - }, "Argument 'name' is of length 1, but should be a unique set of the same length than data.") - - expect_error({ - sspm_discrete %>% - map_dataset(dataset_list, name = c("pred_data", "pred_data"), time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - }, "Argument 'name' is of length 2, but should be a unique set of the same length than data.") - - # Coords 3 cases - expect_error({ - sspm_discrete %>% - map_dataset(dataset_list, name = c("pred_data", "pred_data_2"), - time_column = "year", - uniqueID = "uniqueID", coords = list(c("lon_dec", "lat_dec"), - c("lon_dec", "lat_dec"), - c("lon_dec", "lat_dec"))) - - }, "Argument 'coords' should be a list of the same length than data.") - - expect_error({ - sspm_discrete %>% - map_dataset(list(predator_simulated), name = c("pred_data"), time_column = "year", - uniqueID = "uniqueID", coords = c("lat_dec")) - }, "Argument 'coords' should be of a vector of length 2.") - - expect_error({ - sspm_discrete %>% - map_dataset(dataset_list, name = c("pred_data", "pred_data_2"), time_column = "year", - uniqueID = "uniqueID", coords = list(c("lon_dec", "lat_dec"), - c("lon_dec"))) - }, "Argument 'coords' should be a list of elements of length 2.") - - # One arg too long - expect_error({ - sspm_discrete %>% - map_dataset(dataset_list, name = c("pred_data", "pred_data_2"), - time_column = c("year", "year", "year"), - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - }, "Argument 'time_column' is of length 3. Must be of length 1 or of the same length than data.") - - # Success cases - # Two datasets - expect_class({ - sspm_discrete %>% - map_dataset(dataset_list, name = c("pred_data", "pred_data_2"), time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - }, "sspm_discrete") - - # One dataset - expect_class({ - sspm_discrete %>% - map_dataset(list(predator_simulated), name = "pred_data", time_column = "year", - uniqueID = "uniqueID", coords = c("lon_dec", "lat_dec")) - }, "sspm_discrete") - -}) diff --git a/tests/testthat/test-map-formula.R b/tests/testthat/test-map-formula.R deleted file mode 100644 index cfe31b05..00000000 --- a/tests/testthat/test-map-formula.R +++ /dev/null @@ -1,42 +0,0 @@ -# Test of map_formula - -test_that("Map formula works as expected", { - skip("Deprecated") - - form_mapped <- sspm_discrete_mapped %>% - map_formula(weight_per_km2 ~ smooth_time() + smooth_space() + smooth_space_time(), - "Biomass") - the_form <- translated_formula(spm_formulas(spm_datasets(form_mapped)$Biomass)[[1]]) - - expect_formula(the_form) - expect_match(sspm:::format_formula(the_form), fixed = TRUE, - paste0("weight_per_km2 ~ s(year_f, k = 24L, bs = 're',", - " xt = list(penalty = pen_mat_time)) + ", - "s(patch_id, k = 30, bs = 'mrf', ", - "xt = list(penalty = pen_mat_space)) + ", - "ti(year_f, patch_id, k = c(24, 30), bs = c('re', 'mrf'), ", - "xt = list(year_f = list(penalty = pen_mat_time), ", - "patch_id = list(penalty = pen_mat_space)))")) - - # Error cases - # expect_message({ - # sspm_base %>% map_formula("Biomass", weight_per_km2~smooth_time()) - # }, "Model object 'Model test' is not a discrete model") - - # expect_message({ - # sspm_discrete_mapped %>% map_formula("Biomass") - # }, "Argument 'formula' missing with no default") - - # expect_message({ - # sspm_discrete_mapped %>% map_formula(formula = weight_per_km2~smooth_time()) - # }, "Argument 'dataset' missing with no default") - - expect_error({ - sspm_discrete_mapped %>% map_formula(weight_per_km2 ~ smooth_time(), "NotADataset") - }, "Argument 'dataset' must be one of: Biomass, Predator") - - expect_error({ - sspm_discrete_mapped %>% map_formula(NotAColumn ~ smooth_time(), "Biomass") - }, "The response in the formula is not a column of the dataset.") - -}) From 46010ac7df5b954b618bcd389fe3f7405be96afd Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 16:39:11 -0500 Subject: [PATCH 091/118] update tests and clean --- R/accessors-methods-sspm_formula.R | 35 --------------------------- man/accessors-methods-sspm_formula.Rd | 12 --------- tests/testthat/test-accessors.R | 1 + tests/testthat/test-replacers.R | 15 ++++++++++++ 4 files changed, 16 insertions(+), 47 deletions(-) diff --git a/R/accessors-methods-sspm_formula.R b/R/accessors-methods-sspm_formula.R index bc51877b..f698a8e0 100644 --- a/R/accessors-methods-sspm_formula.R +++ b/R/accessors-methods-sspm_formula.R @@ -89,41 +89,6 @@ setMethod("translated_formula<-", } ) -# Dataset ----------------------------------------------------------------- -# Accessors --------------------------------------------------------------- - -#' @rdname accessors-methods-sspm_formula -#' @export -setGeneric(name = "dataset", - def = function(sspm_object) standardGeneric("dataset") -) - -#' @rdname accessors-methods-sspm_formula -#' @export -setMethod("dataset", - signature("sspm_object" = "sspm_formula"), - function(sspm_object) sspm_object@dataset -) - -# Replacers --------------------------------------------------------------- - -#' @rdname accessors-methods-sspm_formula -#' @export -setGeneric(name = "dataset<-", - def = function(object, value) standardGeneric("dataset<-") -) - -#' @rdname accessors-methods-sspm_formula -#' @export -setMethod("dataset<-", - signature("object" = "sspm_formula"), - function(object, value) { - object@dataset <- value - validObject(object) - return(object) - } -) - # Vars -------------------------------------------------------------------- # Accessors --------------------------------------------------------------- diff --git a/man/accessors-methods-sspm_formula.Rd b/man/accessors-methods-sspm_formula.Rd index 233ac03b..007e0524 100644 --- a/man/accessors-methods-sspm_formula.Rd +++ b/man/accessors-methods-sspm_formula.Rd @@ -9,10 +9,6 @@ \alias{translated_formula,sspm_formula-method} \alias{translated_formula<-} \alias{translated_formula<-,sspm_formula-method} -\alias{dataset} -\alias{dataset,sspm_formula-method} -\alias{dataset<-} -\alias{dataset<-,sspm_formula-method} \alias{formula_vars} \alias{formula_vars,sspm_formula-method} \alias{formula_vars<-} @@ -51,14 +47,6 @@ translated_formula(object) <- value \S4method{translated_formula}{sspm_formula}(object) <- value -dataset(sspm_object) - -\S4method{dataset}{sspm_formula}(sspm_object) - -dataset(object) <- value - -\S4method{dataset}{sspm_formula}(object) <- value - formula_vars(sspm_object) \S4method{formula_vars}{sspm_formula}(sspm_object) diff --git a/tests/testthat/test-accessors.R b/tests/testthat/test-accessors.R index 30933e49..66d5f2ba 100644 --- a/tests/testthat/test-accessors.R +++ b/tests/testthat/test-accessors.R @@ -95,5 +95,6 @@ test_that("Accesors work as expected on `sspm_formula`", { expect_formula(raw_formula(sspm_formula)) expect_formula(translated_formula(sspm_formula)) expect_list(formula_vars(sspm_formula)) + expect_character(formula_type(sspm_formula)) }) diff --git a/tests/testthat/test-replacers.R b/tests/testthat/test-replacers.R index 096f60a8..73868b68 100644 --- a/tests/testthat/test-replacers.R +++ b/tests/testthat/test-replacers.R @@ -100,6 +100,21 @@ test_that("Replacers work as expected", { names(formula_vars(sspm_formula)) }, identical.to = c("a", "b")) + expect_match({ + formula_type(sspm_formula) <- "my_type" + formula_type(sspm_formula) + }, "my_type") + + expect_match({ + spm_lagged_vars(sspm_formula) <- "lag_vars" + spm_lagged_vars(sspm_formula) + }, "lag_vars") + + expect_match({ + spm_response(sspm_formula) <- "response" + spm_response(sspm_formula) + }, "response") + # Dataset expect_data_frame({ From d67e1fdef4faf1fd17c0c6fe6b29dc5653a66d3f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 16:56:58 -0500 Subject: [PATCH 092/118] add tests --- tests/testthat/helper-tests.R | 4 +++- tests/testthat/test-accessors.R | 9 +++++++++ tests/testthat/test-replacers.R | 32 ++++++++++++++++++++++++++++++++ 3 files changed, 44 insertions(+), 1 deletion(-) diff --git a/tests/testthat/helper-tests.R b/tests/testthat/helper-tests.R index 92a709cb..0a8de817 100644 --- a/tests/testthat/helper-tests.R +++ b/tests/testthat/helper-tests.R @@ -138,10 +138,12 @@ sspm_model <- new("sspm", smoothed_data = borealis_spatial, is_split = FALSE) +fit_bam <- bam(data = mtcars, mpg ~ wt, family = gaussian) + sspm_fit <- new("sspm_fit", smoothed_data = all_data, time = spm_time(biomass_dataset_smoothed), uniqueID = spm_unique_ID(biomass_dataset_smoothed), formula = sspm_formula, boundaries = spm_boundaries(biomass_dataset_smoothed), - fit = bam(data = mtcars, mpg ~ wt, family = gaussian)) + fit = fit_bam) diff --git a/tests/testthat/test-accessors.R b/tests/testthat/test-accessors.R index 66d5f2ba..2595cf31 100644 --- a/tests/testthat/test-accessors.R +++ b/tests/testthat/test-accessors.R @@ -98,3 +98,12 @@ test_that("Accesors work as expected on `sspm_formula`", { expect_character(formula_type(sspm_formula)) }) + +test_that("Accesors work as expected on `sspm_fit`", { + + expect_character(spm_unique_ID(sspm_fit)) + expect_character(spm_time(sspm_fit)) + expect_class(spm_get_fit(sspm_fit), "bam") + +}) + diff --git a/tests/testthat/test-replacers.R b/tests/testthat/test-replacers.R index 73868b68..72457f39 100644 --- a/tests/testthat/test-replacers.R +++ b/tests/testthat/test-replacers.R @@ -176,4 +176,36 @@ test_that("Replacers work as expected", { }, c("weight_per_km2", "weight_per_km2_replaced")) + ## Fit + + expect_equal({ + spm_unique_ID(sspm_fit) <- "uniqueID" + spm_unique_ID(sspm_fit) + }, "uniqueID") + + expect_equal({ + spm_time(sspm_fit) <- "timevar" + spm_time(sspm_fit) + }, "timevar") + + expect_equal({ + spm_formulas(sspm_fit) <- sspm_formula + spm_formulas(sspm_fit) + }, sspm_formula) + + expect_equal({ + spm_get_fit(sspm_fit) <- fit_bam + spm_get_fit(sspm_fit) + }, fit_bam) + + expect_equal({ + spm_boundaries(sspm_fit) <- spm_boundaries(biomass_dataset_smoothed) + spm_boundaries(sspm_fit) + }, spm_boundaries(biomass_dataset_smoothed)) + + expect_equal({ + spm_boundary(sspm_fit) <- "sfa" + spm_boundary(sspm_fit) + }, "sfa") + }) From 23986d31957663be642cd741fed21ce2de3e128e Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 16:59:09 -0500 Subject: [PATCH 093/118] rename file --- tests/testthat/{test-z_integration.R => test-zzzintegration.R} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/testthat/{test-z_integration.R => test-zzzintegration.R} (100%) diff --git a/tests/testthat/test-z_integration.R b/tests/testthat/test-zzzintegration.R similarity index 100% rename from tests/testthat/test-z_integration.R rename to tests/testthat/test-zzzintegration.R From d0497d10bcea9b429778dd2285352e3e5c7298e5 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 17:04:27 -0500 Subject: [PATCH 094/118] export changes --- NAMESPACE | 6 ------ 1 file changed, 6 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index da56d2f4..fbecb823 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,6 @@ # Generated by roxygen2: do not edit by hand export("%>%") -export("dataset<-") export("formula_type<-") export("formula_vars<-") export("is_fitted<-") @@ -33,7 +32,6 @@ export("spm_time<-") export("spm_unique_ID<-") export("translated_formula<-") export(as_discretization_method) -export(dataset) export(formula_type) export(formula_vars) export(is_fitted) @@ -41,7 +39,6 @@ export(is_mapped) export(is_split) export(method_func) export(plot) -export(plot_trajectory) export(predict) export(predict_intervals) export(raw_formula) @@ -87,7 +84,6 @@ export(tesselate_voronoi) export(translated_formula) export(triangulate_delaunay) exportMethods("$") -exportMethods("dataset<-") exportMethods("formula_type<-") exportMethods("formula_vars<-") exportMethods("is_fitted<-") @@ -119,7 +115,6 @@ exportMethods("spm_time<-") exportMethods("spm_unique_ID<-") exportMethods("translated_formula<-") exportMethods(as_discretization_method) -exportMethods(dataset) exportMethods(formula_type) exportMethods(formula_vars) exportMethods(is_fitted) @@ -127,7 +122,6 @@ exportMethods(is_mapped) exportMethods(is_split) exportMethods(method_func) exportMethods(plot) -exportMethods(plot_trajectory) exportMethods(raw_formula) exportMethods(smooth_lag) exportMethods(smooth_space) From c8340988168dd9c04c72fba4bb1e5c58a2741c2b Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 17:14:13 -0500 Subject: [PATCH 095/118] imorive tests --- R/methods-extract.R | 15 --------------- man/extract-methods.Rd | 3 --- tests/testthat/test-extracts.R | 12 ++++++++++++ 3 files changed, 12 insertions(+), 18 deletions(-) diff --git a/R/methods-extract.R b/R/methods-extract.R index cf671c7c..ea08450b 100644 --- a/R/methods-extract.R +++ b/R/methods-extract.R @@ -64,18 +64,3 @@ setMethod("$", } } ) - -#' @export -#' @rdname extract-methods -setMethod("$", - "sspm_fit", - function(x, name) { - if (is.null(x@smoothed_data)) { - x@data %>% - dplyr::select(c(name, spm_time(x), "geometry")) - } else { - x@smoothed_data %>% - dplyr::select(c(name, spm_time(x), "geometry")) - } - } -) diff --git a/man/extract-methods.Rd b/man/extract-methods.Rd index bfe03e0c..4d9e5085 100644 --- a/man/extract-methods.Rd +++ b/man/extract-methods.Rd @@ -6,7 +6,6 @@ \alias{$,sspm_discrete_boundary-method} \alias{$,sspm_dataset-method} \alias{$,sspm-method} -\alias{$,sspm_fit-method} \title{Extract methods} \usage{ \S4method{$}{sspm_boundary}(x, name) @@ -16,8 +15,6 @@ \S4method{$}{sspm_dataset}(x, name) \S4method{$}{sspm}(x, name) - -\S4method{$}{sspm_fit}(x, name) } \arguments{ \item{x}{\strong{[sspm_...]} An object from this package.} diff --git a/tests/testthat/test-extracts.R b/tests/testthat/test-extracts.R index e69de29b..cdd48d91 100644 --- a/tests/testthat/test-extracts.R +++ b/tests/testthat/test-extracts.R @@ -0,0 +1,12 @@ +# Extract methods + +test_that("Extractors work as expected", { + + expect_class(boundary$sfa, "sf") + expect_class(boundary_discrete$sfa, "sf") + expect_class(biomass_dataset$sfa, "sf") + expect_class(biomass_dataset_smoothed$sfa, "sf") + expect_class(sspm_model$sfa, "sf") + expect_class(sspm_fit$sfa, "sf") + +}) From 56d0d681e1b0427a63debb2c58a98a7ccf95fb74 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 17:21:36 -0500 Subject: [PATCH 096/118] remove topic --- _pkgdown.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index e23f88a3..ad8b5c7e 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -65,7 +65,6 @@ reference: - predict - predict_intervals - plot - - plot_trajectory - title: Datasets desc: Datasets used in the vignette From d9e5e06faf83ab33456ed7d6f7680ac35ddf79d5 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 17:26:23 -0500 Subject: [PATCH 097/118] clean up tests --- tests/testthat/test-plot.R | 6 --- tests/testthat/test-smooths.R | 99 ----------------------------------- 2 files changed, 105 deletions(-) delete mode 100644 tests/testthat/test-plot.R diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R deleted file mode 100644 index 53261d32..00000000 --- a/tests/testthat/test-plot.R +++ /dev/null @@ -1,6 +0,0 @@ -# Simple tests for plotting functions - -test_that("Plotting works as expected", { - skip("TODO") - -}) diff --git a/tests/testthat/test-smooths.R b/tests/testthat/test-smooths.R index c866dfe7..84c9ee2a 100644 --- a/tests/testthat/test-smooths.R +++ b/tests/testthat/test-smooths.R @@ -39,102 +39,3 @@ test_that("Smooths are assembles correctly", { "s(a = \"ns\", b = 3, c = list(d = \"hn\", e = 4))") }) - -test_that("Main smooth functions work as expected", { - skip("TODO") - - # Failures => This avctually test ICAR - expect_error(smooth_time(dataset = "NotValid", sspm_object = sspm_discrete_mapped), - "Argument 'dataset' must be one of: Biomass, Predator") - - # Successes - res_time <- smooth_time(dataset = "Biomass", sspm_object = sspm_discrete_mapped) - expect_equal(res_time$smooth, "s(year_f, k = 24L, bs = \"re\", xt = list(penalty = pen_mat_time))") - expect_names(names(res_time$vars), identical.to = c("pen_mat_time")) - expect_equal(dim(res_time$vars$pen_mat_time), c(24, 24)) - - res_space <- smooth_space(dataset = "Biomass", sspm_object = sspm_discrete_mapped) - expect_equal(res_space$smooth, "s(patch_id, k = 30, bs = \"mrf\", xt = list(penalty = pen_mat_space))") - expect_names(names(res_space$vars), identical.to = c("pen_mat_space")) - expect_equal(dim(res_space$vars$pen_mat_space), c(69, 69)) - - res_space_time <- smooth_space_time(dataset = "Biomass", sspm_object = sspm_discrete_mapped) - expect_equal(res_space_time$smooth, - paste0("ti(year_f, patch_id, k = c(24, 30), ", - "bs = c(\"re\", \"mrf\"), xt = list(year_f = list(penalty = pen_mat_time), ", - "patch_id = list(penalty = pen_mat_space)))")) - expect_names(names(res_space_time$vars), identical.to = c("pen_mat_time", "pen_mat_space")) - expect_equal(dim(res_space_time$vars$pen_mat_time), c(24, 24)) - expect_equal(dim(res_space_time$vars$pen_mat_space), c(69, 69)) - - # res_lag <- smooth_lag(dataset = "Biomass", sspm_object = sspm_discrete_mapped) - -}) - -test_that("ICAR function works as expected", { - skip("TODO") - - # Just basic output - res_ICAR <- sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "time", - k = NULL, xt = NULL, bs = NULL) - expect_equal(res_ICAR$args[[1]], substitute(year_f)) - expect_equal(res_ICAR$args$k, 24) - expect_equal(res_ICAR$args$bs, "re") - expect_equal(res_ICAR$args$xt, alist(penalty = pen_mat_time)) - - res_ICAR <- sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space", - k = NULL, xt = NULL, bs = NULL) - expect_equal(res_ICAR$args[[1]], substitute(patch_id)) - expect_equal(res_ICAR$args$k, 30) - expect_equal(res_ICAR$args$bs, "mrf") - expect_equal(res_ICAR$args$xt, alist(penalty = pen_mat_space)) - - res_ICAR <- sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = NULL, bs = NULL) - expect_equal(res_ICAR$args[[1]], substitute(year_f)) - expect_equal(res_ICAR$args[[2]], substitute(patch_id)) - expect_equal(res_ICAR$args$k, c(24, 30)) - expect_equal(res_ICAR$args$bs, c("re", "mrf")) - expect_equal(res_ICAR$args$xt, list(year_f = alist(penalty = pen_mat_time), - patch_id = alist(penalty = pen_mat_space))) - - # Now test xt cases - # Cannot handle cases where other arguments are passed as list in xt - test_mat <- matrix(0, 5, 5) - - expect_identical({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space", - k = NULL, xt = list(penalty = test_mat), bs = NULL)$vars$pen_mat_space$penalty - }, test_mat) - - expect_identical({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "time", - k = NULL, xt = list(penalty = test_mat), bs = NULL)$vars$pen_mat_time$penalty - }, test_mat) - - expect_identical({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = list(patch_id = list(penalty = matrix(0, 5, 5))), bs = NULL)$vars$pen_mat_space - }, test_mat) - - expect_identical({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = list(year_f = list(penalty = matrix(0, 5, 5))), bs = NULL)$vars$pen_mat_time - }, test_mat) - - # Failures - expect_error({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = list(year_f = list(penalty = mtcars)), bs = NULL) - }, "Must be of type 'matrix', not 'data.frame'") - - expect_error({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = list(list(penalty = test_mat)), bs = NULL) - }, "failed: Must have names.") - - expect_error({ - sspm:::ICAR(sspm_discrete_mapped, "Biomass", dimension = "space_time", - k = NULL, xt = list(penalty = test_mat), bs = NULL) - }, "failed: Must be of type 'list', not 'matrix'.") -}) From a1097d3f31b00685ff3d98ab83f8935ab3583cf3 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 17:41:47 -0500 Subject: [PATCH 098/118] upgrade tests --- tests/testthat/test-extracts.R | 1 - tests/testthat/test-zzzintegration.R | 3 ++- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-extracts.R b/tests/testthat/test-extracts.R index cdd48d91..d01c4aa3 100644 --- a/tests/testthat/test-extracts.R +++ b/tests/testthat/test-extracts.R @@ -7,6 +7,5 @@ test_that("Extractors work as expected", { expect_class(biomass_dataset$sfa, "sf") expect_class(biomass_dataset_smoothed$sfa, "sf") expect_class(sspm_model$sfa, "sf") - expect_class(sspm_fit$sfa, "sf") }) diff --git a/tests/testthat/test-zzzintegration.R b/tests/testthat/test-zzzintegration.R index 8059fda3..abd5008c 100644 --- a/tests/testthat/test-zzzintegration.R +++ b/tests/testthat/test-zzzintegration.R @@ -73,7 +73,8 @@ test_that("Worklow works as expected", { spm(log_productivity ~ sfa + weight_per_km2_all_predators_lag_1 + smooth_space(by = weight_per_km2_borealis_lag_1) + - smooth_space(), + smooth_space() + + smooth_lag("weight_per_km2_borealis"), family = mgcv::scat) gam_fit <- spm_get_fit(sspm_model_fit) From 2d2c6a3c9e4250f68a962e23be0309ac2c2a821f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 18:14:58 -0500 Subject: [PATCH 099/118] fix aggregate --- R/spm_aggregate.R | 70 ++++++++++++++++----------------- man/spm_aggregate.Rd | 14 ------- tests/testthat/test-aggregate.R | 15 +++++++ 3 files changed, 50 insertions(+), 49 deletions(-) create mode 100644 tests/testthat/test-aggregate.R diff --git a/R/spm_aggregate.R b/R/spm_aggregate.R index a71202e7..1d79fd1e 100644 --- a/R/spm_aggregate.R +++ b/R/spm_aggregate.R @@ -138,38 +138,38 @@ setMethod(f = "spm_aggregate", } ) -#' @export -#' @rdname spm_aggregate -setMethod(f = "spm_aggregate", - signature(sspm_object = "sspm_fit"), - function(sspm_object, - variable, - fun, - group_by, - fill, - apply_to_df, - ...){ - - # Check info - checkmate::assert_character(variable) - checkmate::assert_function(fun) - checkmate::assert_choice(group_by, spm_aggregation_choices()) - - # Get info - dataset_data <- spm_smoothed_data(sspm_object) - bounds <- spm_boundaries(sspm_object) - boundary <- spm_boundary(bounds) - time_col <- spm_time(sspm_object) - - # Verify column - assert_column(dataset_data, variable) - - spm_smoothed_data(sspm_object) <- - spm_aggregate_routine(dataset_data, bounds, group_by, "boundary", - time_col, boundary, variable, fun, fill, - apply_to_df, ...) - - return(sspm_object) - - } -) +# # @export +# # @rdname spm_aggregate +# setMethod(f = "spm_aggregate", +# signature(sspm_object = "sspm_fit"), +# function(sspm_object, +# variable, +# fun, +# group_by, +# fill, +# apply_to_df, +# ...){ +# +# # Check info +# checkmate::assert_character(variable) +# checkmate::assert_function(fun) +# checkmate::assert_choice(group_by, spm_aggregation_choices()) +# +# # Get info +# dataset_data <- spm_smoothed_data(sspm_object) +# bounds <- spm_boundaries(sspm_object) +# boundary <- spm_boundary(bounds) +# time_col <- spm_time(sspm_object) +# +# # Verify column +# assert_column(dataset_data, variable) +# +# spm_smoothed_data(sspm_object) <- +# spm_aggregate_routine(dataset_data, bounds, group_by, "boundary", +# time_col, boundary, variable, fun, fill, +# apply_to_df, ...) +# +# return(sspm_object) +# +# } +# ) diff --git a/man/spm_aggregate.Rd b/man/spm_aggregate.Rd index ae034ad4..f6905627 100644 --- a/man/spm_aggregate.Rd +++ b/man/spm_aggregate.Rd @@ -4,7 +4,6 @@ \alias{spm_aggregate} \alias{spm_aggregate,sspm_dataset,missing-method} \alias{spm_aggregate,sspm_dataset,sspm_discrete_boundary-method} -\alias{spm_aggregate,sspm_fit,ANY-method} \title{Aggregate a dataset or fit data variable based on a boundary} \usage{ spm_aggregate( @@ -45,19 +44,6 @@ spm_aggregate( apply_to_df = FALSE, ... ) - -\S4method{spm_aggregate}{sspm_fit,ANY}( - sspm_object, - boundaries, - level = "patch", - type = "data", - variable, - fun, - group_by = "spacetime", - fill = FALSE, - apply_to_df = FALSE, - ... -) } \arguments{ \item{sspm_object}{\strong{[sspm_dataset or sspm_fit]} The dataset object.} diff --git a/tests/testthat/test-aggregate.R b/tests/testthat/test-aggregate.R new file mode 100644 index 00000000..89ddc229 --- /dev/null +++ b/tests/testthat/test-aggregate.R @@ -0,0 +1,15 @@ +# Test aggregation + +test_that("Aggregation works as expected", { + + expect_class({ + spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + fun = mean, type = "smoothed", group_by = "time") + }, "sspm_dataset") + + expect_class({ + spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + fun = mean, type = "smoothed", group_by = "space") + }, "sspm_dataset") + +}) From ce137c7f26c84a86b88c0526712435b6aee1b10f Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 18:53:41 -0500 Subject: [PATCH 100/118] add tests for units --- R/units.R | 48 ++++++++++++++++----------------- tests/testthat/test-aggregate.R | 24 +++++++++++++++++ 2 files changed, 48 insertions(+), 24 deletions(-) diff --git a/R/units.R b/R/units.R index 9f3c2b99..61bf9c3e 100644 --- a/R/units.R +++ b/R/units.R @@ -26,13 +26,13 @@ is_biomass <- function(x){ is_units(x) && has_biomass_units(x) } -is_area <- function(x){ - is_units(x) && has_area_units(x) -} +# is_area <- function(x){ +# is_units(x) && has_area_units(x) +# } -is_biomass_density <- function(x){ - is_units(x) && has_biomass_density_units(x) -} +# is_biomass_density <- function(x){ +# is_units(x) && has_biomass_density_units(x) +# } has_biomass_units <- function(x){ if (is_units(x)){ @@ -44,22 +44,22 @@ has_biomass_units <- function(x){ } } -has_biomass_density_units <- function(x){ - if (is_units(x)){ - num <- units(x)$numerator - den <- units(x)$denominator - (length(num) == 1) && (length(den) == 2) && (num == "kg") && (den == c("km", "km")) - } else { - FALSE - } -} +# has_biomass_density_units <- function(x){ +# if (is_units(x)){ +# num <- units(x)$numerator +# den <- units(x)$denominator +# (length(num) == 1) && (length(den) == 2) && (num == "kg") && (den == c("km", "km")) +# } else { +# FALSE +# } +# } -has_area_units <- function(x){ - if (is_units(x)){ - num <- units(x)$numerator - den <- units(x)$denominator - (length(num) == 2) && (length(den) == 0) && (num == c("km", "km")) - } else { - FALSE - } -} +# has_area_units <- function(x){ +# if (is_units(x)){ +# num <- units(x)$numerator +# den <- units(x)$denominator +# (length(num) == 2) && (length(den) == 0) && (num == c("km", "km")) +# } else { +# FALSE +# } +# } diff --git a/tests/testthat/test-aggregate.R b/tests/testthat/test-aggregate.R index 89ddc229..d25fea4e 100644 --- a/tests/testthat/test-aggregate.R +++ b/tests/testthat/test-aggregate.R @@ -7,9 +7,33 @@ test_that("Aggregation works as expected", { fun = mean, type = "smoothed", group_by = "time") }, "sspm_dataset") + expect_class({ + spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + fun = mean, type = "smoothed", group_by = "space", + level = "boundary") + }, "sspm_dataset") + + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # apply_to_df = TRUE) + # }, "sspm_dataset") + expect_class({ spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", fun = mean, type = "smoothed", group_by = "space") }, "sspm_dataset") + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # fill = NA) + # }, "sspm_dataset") + + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # fill = 0) + # }, "sspm_dataset") + }) From 43718c9518137a2503ea7d0ac99f35ff05df8cc4 Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 18:53:41 -0500 Subject: [PATCH 101/118] add tests for units closes #57 and closes #30 --- R/units.R | 48 ++++++++++++++++----------------- tests/testthat/test-aggregate.R | 24 +++++++++++++++++ 2 files changed, 48 insertions(+), 24 deletions(-) diff --git a/R/units.R b/R/units.R index 9f3c2b99..61bf9c3e 100644 --- a/R/units.R +++ b/R/units.R @@ -26,13 +26,13 @@ is_biomass <- function(x){ is_units(x) && has_biomass_units(x) } -is_area <- function(x){ - is_units(x) && has_area_units(x) -} +# is_area <- function(x){ +# is_units(x) && has_area_units(x) +# } -is_biomass_density <- function(x){ - is_units(x) && has_biomass_density_units(x) -} +# is_biomass_density <- function(x){ +# is_units(x) && has_biomass_density_units(x) +# } has_biomass_units <- function(x){ if (is_units(x)){ @@ -44,22 +44,22 @@ has_biomass_units <- function(x){ } } -has_biomass_density_units <- function(x){ - if (is_units(x)){ - num <- units(x)$numerator - den <- units(x)$denominator - (length(num) == 1) && (length(den) == 2) && (num == "kg") && (den == c("km", "km")) - } else { - FALSE - } -} +# has_biomass_density_units <- function(x){ +# if (is_units(x)){ +# num <- units(x)$numerator +# den <- units(x)$denominator +# (length(num) == 1) && (length(den) == 2) && (num == "kg") && (den == c("km", "km")) +# } else { +# FALSE +# } +# } -has_area_units <- function(x){ - if (is_units(x)){ - num <- units(x)$numerator - den <- units(x)$denominator - (length(num) == 2) && (length(den) == 0) && (num == c("km", "km")) - } else { - FALSE - } -} +# has_area_units <- function(x){ +# if (is_units(x)){ +# num <- units(x)$numerator +# den <- units(x)$denominator +# (length(num) == 2) && (length(den) == 0) && (num == c("km", "km")) +# } else { +# FALSE +# } +# } diff --git a/tests/testthat/test-aggregate.R b/tests/testthat/test-aggregate.R index 89ddc229..d25fea4e 100644 --- a/tests/testthat/test-aggregate.R +++ b/tests/testthat/test-aggregate.R @@ -7,9 +7,33 @@ test_that("Aggregation works as expected", { fun = mean, type = "smoothed", group_by = "time") }, "sspm_dataset") + expect_class({ + spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + fun = mean, type = "smoothed", group_by = "space", + level = "boundary") + }, "sspm_dataset") + + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # apply_to_df = TRUE) + # }, "sspm_dataset") + expect_class({ spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", fun = mean, type = "smoothed", group_by = "space") }, "sspm_dataset") + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # fill = NA) + # }, "sspm_dataset") + + # expect_class({ + # spm_aggregate(biomass_dataset_smoothed, variable = "weight_per_km2", + # fun = mean, type = "smoothed", group_by = "space", + # fill = 0) + # }, "sspm_dataset") + }) From a715716f9b5b75f2e2ffea9175fc6366bc766d9e Mon Sep 17 00:00:00 2001 From: vlucet Date: Sun, 29 Jan 2023 23:11:29 -0500 Subject: [PATCH 102/118] remove line --- tests/testthat/test-sspm.R | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/testthat/test-sspm.R b/tests/testthat/test-sspm.R index e0948436..5c028909 100644 --- a/tests/testthat/test-sspm.R +++ b/tests/testthat/test-sspm.R @@ -15,7 +15,6 @@ test_that("sspm object are created correcly", { expect_class(sspm_model_split, "sspm") expect_true(is_split(sspm_model_split)) - sspm_model_lag <- sspm_model_split %>% spm_lag(vars = c("weight_per_km2_biomass_test", "weight_per_km2_predator_test"), From e1cabe5d5b66461f2c589718e0ee4df9f020413b Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 01:02:59 -0500 Subject: [PATCH 103/118] update to tidyselect 1.2 --- R/discretize-helpers.R | 8 ++++---- R/intervals.R | 20 ++++++++++---------- R/methods-extract.R | 12 ++++++------ R/predict-helpers.R | 30 +++++++++++++++--------------- R/smooths-helpers.R | 4 ++-- R/spm_aggregate-helpers.R | 6 +++--- R/spm_aggregate_catch.R | 8 ++++---- R/spm_as_boundary.R | 8 ++++---- R/spm_as_dataset.R | 4 ++-- R/spm_lag.R | 2 +- R/spm_smooth.R | 2 +- R/spm_split.R | 4 ++-- R/sspm.R | 4 ++-- renv.lock | 20 +++++++++++--------- 14 files changed, 67 insertions(+), 65 deletions(-) diff --git a/R/discretize-helpers.R b/R/discretize-helpers.R index d34b74be..e6ce6945 100644 --- a/R/discretize-helpers.R +++ b/R/discretize-helpers.R @@ -68,7 +68,7 @@ sample_points <- function(mode, with, boundaries, boundary, nb_samples, seed){ nb_samples = nb_samples) %>% lapply(sf::st_as_sf) %>% dplyr::bind_rows() %>% - dplyr::rename(geometry = .data$x) %>% + dplyr::rename(geometry = "x") %>% sf::st_join(boundaries) } else if (mode == "points") { @@ -121,7 +121,7 @@ make_patches_voronoi <- function(stratify, voronoi_points, boundaries, boundary) dplyr::bind_rows() voronoi <- sf::st_sf(voronoi) %>% - dplyr::rename(geometry = .data$x) + dplyr::rename(geometry = "x") } else { @@ -196,7 +196,7 @@ cleanup_polygons <- function(pols, boundary){ # Add an area column pols <- dplyr::mutate(pols, patch_area = units::set_units(sf::st_area(pols), value = "km^2")) %>% - dplyr::relocate(.data$patch_area, .before = .data$geometry) + dplyr::relocate("patch_area", .before = "geometry") # Add a patch_id column pols <- @@ -216,6 +216,6 @@ make_base_from_bounds <- function(boundaries){ sf::st_cast() %>% sf::st_cast("POLYGON") %>% sf::st_as_sf() %>% - dplyr::rename(geometry = .data$x) + dplyr::rename(geometry = "x") } diff --git a/R/intervals.R b/R/intervals.R index 342e5789..5e6e3c1b 100644 --- a/R/intervals.R +++ b/R/intervals.R @@ -94,8 +94,8 @@ predict_biomass_intervals <- function(object_fit, patches, smoothed_data, time_c biomass_PI_upper = .data$biomass_density_upper_P * .data[[patch_area_col]]) %>% - dplyr::select(.data$biomass_CI_lower, .data$biomass_CI_upper, - .data$biomass_PI_lower, .data$biomass_PI_upper) + dplyr::select("biomass_CI_lower", "biomass_CI_upper", + "biomass_PI_lower", "biomass_PI_upper") } else { @@ -107,8 +107,8 @@ predict_biomass_intervals <- function(object_fit, patches, smoothed_data, time_c # Bind all CI_df <- smoothed_data %>% sf::st_drop_geometry() %>% - dplyr::select(.data[[biomass]], .data$patch_id, .data[[time_col]], - .data[[bounds_col]], .data[[patch_area_col]]) %>% + dplyr::select(all_of(biomass), "patch_id", all_of(time_col), + all_of(bounds_col), all_of(patch_area_col)) %>% dplyr::bind_cols(CI_df_prod) %>% @@ -132,15 +132,15 @@ predict_biomass_intervals <- function(object_fit, patches, smoothed_data, time_c # CI dplyr::mutate(biomass_density_lower = .data$biomass_density_with_catch_lower - - dplyr::all_of(catch_density), + catch_density, biomass_density_upper = .data$biomass_density_with_catch_upper - - dplyr::all_of(catch_density), + catch_density, # PI biomass_density_lower_P = .data$biomass_density_with_catch_lower_P - - dplyr::all_of(catch_density), + catch_density, biomass_density_upper_P = .data$biomass_density_with_catch_upper_P - - dplyr::all_of(catch_density)) %>% + catch_density) %>% dplyr::mutate( # biomass_with_catch_lower = .data$biomass_density_with_catch_lower * @@ -162,8 +162,8 @@ predict_biomass_intervals <- function(object_fit, patches, smoothed_data, time_c dplyr::ungroup() %>% - dplyr::select(.data$biomass_CI_lower, .data$biomass_CI_upper, - .data$biomass_PI_lower, .data$biomass_PI_upper) + dplyr::select("biomass_CI_lower", "biomass_CI_upper", + "biomass_PI_lower", "biomass_PI_upper") } diff --git a/R/methods-extract.R b/R/methods-extract.R index ea08450b..49635427 100644 --- a/R/methods-extract.R +++ b/R/methods-extract.R @@ -21,7 +21,7 @@ setMethod("$", "sspm_boundary", function(x, name) { x@boundaries %>% - dplyr::select(c(name, spm_boundary(x), "geometry")) + dplyr::select(c(all_of(name), spm_boundary(x), "geometry")) } ) @@ -31,7 +31,7 @@ setMethod("$", "sspm_discrete_boundary", function(x, name) { x@boundaries %>% - dplyr::select(c(name, spm_boundary(x), "geometry")) + dplyr::select(c(all_of(name), spm_boundary(x), "geometry")) } ) @@ -42,10 +42,10 @@ setMethod("$", function(x, name) { if (is.null(x@smoothed_data)) { x@data %>% - dplyr::select(c(name, spm_time(x), "geometry")) + dplyr::select(c(all_of(name), spm_time(x), "geometry")) } else { x@smoothed_data %>% - dplyr::select(c(name, spm_time(x), "geometry")) + dplyr::select(c(all_of(name), spm_time(x), "geometry")) } } ) @@ -57,10 +57,10 @@ setMethod("$", function(x, name) { if (is.null(x@smoothed_data)) { x@data %>% - dplyr::select(c(name, spm_time(x), "geometry")) + dplyr::select(c(all_of(name), spm_time(x), "geometry")) } else { x@smoothed_data %>% - dplyr::select(c(name, spm_time(x), "geometry")) + dplyr::select(c(all_of(name), spm_time(x), "geometry")) } } ) diff --git a/R/predict-helpers.R b/R/predict-helpers.R index 262ffae2..8b335fd1 100644 --- a/R/predict-helpers.R +++ b/R/predict-helpers.R @@ -32,7 +32,7 @@ predict_productivity <- function(object, new_data, type, interval){ # Keep the minimum column set columns_to_keep <- smoothed_data %>% - dplyr::select(.data$patch_id, !!time_col, + dplyr::select("patch_id", !!time_col, !!bounds_col, !!patch_area_col) # Bind and turn into sf object @@ -84,10 +84,10 @@ predict_biomass <- function(object, new_data, biomass, next_ts, preds <- predict(object) preds_df <- smoothed_data %>% - dplyr::select(.data[[time_col]], .data$patch_id, .data[[biomass]], - .data$catch_density, .data[[bounds_col]]) %>% + dplyr::select(all_of(time_col), "patch_id", all_of(biomass), + "catch_density", all_of(bounds_col)) %>% dplyr::left_join(sf::st_drop_geometry(preds), - by = c(dplyr::all_of(c(time_col, bounds_col)), "patch_id")) %>% + by = c(c(time_col, bounds_col), "patch_id")) %>% dplyr::group_by(.data$patch_id, .data[[bounds_col]]) %>% dplyr::mutate(biomass_density_with_catch = @@ -97,16 +97,16 @@ predict_biomass <- function(object, new_data, biomass, next_ts, dplyr::ungroup() %>% - dplyr::select(-.data$pred, -.data$pred_log, -.data[[biomass]], - -.data$catch_density) %>% - dplyr::relocate(.data[[patch_area_col]], .before = "geometry") %>% + dplyr::select(-"pred", -"pred_log", -all_of(biomass), + -"catch_density") %>% + dplyr::relocate(all_of(patch_area_col), .before = "geometry") %>% dplyr::mutate(biomass_with_catch = .data$biomass_density_with_catch * .data[[patch_area_col]], biomass = .data$biomass_density * .data[[patch_area_col]]) %>% - dplyr::relocate(.data$biomass_with_catch, .data$biomass, - .data$biomass_density_with_catch, .data$biomass_density, + dplyr::relocate("biomass_with_catch", "biomass", + "biomass_density_with_catch", "biomass_density", .before = "geometry") %>% sf::st_as_sf() @@ -199,14 +199,14 @@ predict_next_ts <- function(object, new_data, biomass){ dplyr::mutate(density_next_ts = density_last_year * ratio_next_ts, biomass = .data$density_next_ts * .data[[patch_area_col]], year_f = next_ts_data$next_ts) %>% - dplyr::select(-.data$density_next_ts) + dplyr::select(-"density_next_ts") # Cosmetic changes preds_df <- preds_df %>% - dplyr::relocate(.data$biomass) %>% - dplyr::relocate(.data[[bounds_col]]) %>% - dplyr::relocate(.data[[time_col]]) %>% - dplyr::relocate(.data$patch_id) + dplyr::relocate("biomass") %>% + dplyr::relocate(all_of(bounds_col)) %>% + dplyr::relocate(all_of(time_col)) %>% + dplyr::relocate("patch_id") return(list(preds_df = preds_df, new_data = new_data)) @@ -229,7 +229,7 @@ make_next_ts_data <- function(object, time_col, patches, bounds_col, # Make a new grid with the next timestep new_grid <- patches %>% - dplyr::select(.data[[bounds_col]], .data$patch_id) %>% + dplyr::select(all_of(bounds_col), "patch_id") %>% tidyr::expand_grid(!!time_col := next_ts) # Bind the new grid to the smooth data diff --git a/R/smooths-helpers.R b/R/smooths-helpers.R index d3cc1641..90e1a6c0 100644 --- a/R/smooths-helpers.R +++ b/R/smooths-helpers.R @@ -26,14 +26,14 @@ make_by_matrix <- function(data_frame, k, boundaries, time, var){ by_mat <- data_frame %>% sf::st_set_geometry(NULL) %>% - dplyr::select(.data$patch_id, !!boundary_col, !!time, !!var) %>% + dplyr::select("patch_id", !!boundary_col, !!time, !!var) %>% dplyr::nest_by(.data$patch_id, !!boundary_col := .data[[boundary_col]]) %>% dplyr::mutate(lags = list(multilag(variable = .data$data[[var]], n_lags = k, # TODO: assuming in-group mean as default default = mean(.data$data[[var]], na.rm = T)))) %>% - tidyr::unnest(cols = c(.data$lags, .data$data)) %>% + tidyr::unnest(cols = c("lags", "data")) %>% dplyr::ungroup() %>% dplyr::select(dplyr::contains('lag')) %>% dplyr::select(-dplyr::contains(var)) %>% diff --git a/R/spm_aggregate-helpers.R b/R/spm_aggregate-helpers.R index 81105f4f..2594cf0d 100644 --- a/R/spm_aggregate-helpers.R +++ b/R/spm_aggregate-helpers.R @@ -58,14 +58,14 @@ spm_aggregate_routine <- function(dataset_data, boundaries, group_by, level, if (level == "patch"){ dataset_data_tmp <- dataset_data_tmp %>% - dplyr::mutate(temp = as.numeric(.data$temp)) %>% + dplyr::mutate(temp = as.numeric(temp)) %>% tidyr::complete(.data[[time_col]], .data$patch_id, fill = list(temp = as.numeric(fill_value))) } else if (level == "boundary"){ dataset_data_tmp <- dataset_data_tmp %>% - dplyr::mutate(temp = as.numeric(.data$temp)) %>% + dplyr::mutate(temp = as.numeric(temp)) %>% tidyr::complete(.data[[time_col]], .data[[boundary]], fill = list(temp = as.numeric(fill_value))) @@ -77,7 +77,7 @@ spm_aggregate_routine <- function(dataset_data, boundaries, group_by, level, # Rename before returning dataset_data_tmp <- dataset_data_tmp %>% - dplyr::rename(!!variable := .data$temp) + dplyr::rename(!!variable := "temp") return(dataset_data_tmp) diff --git a/R/spm_aggregate_catch.R b/R/spm_aggregate_catch.R index 302753a5..50482d0f 100644 --- a/R/spm_aggregate_catch.R +++ b/R/spm_aggregate_catch.R @@ -81,7 +81,7 @@ setMethod(f = "spm_aggregate_catch", na.rm = TRUE, ...) catch_data <- spm_data(catch) %>% - dplyr::rename(!!biomass_time_col := catch_time_col) + dplyr::rename(!!biomass_time_col := all_of(catch_time_col)) # First, join data full_smoothed_data <- spm_smoothed_data(biomass) %>% @@ -104,7 +104,7 @@ setMethod(f = "spm_aggregate_catch", # Calculate productivity full_smoothed_data <- full_smoothed_data %>% - dplyr::rename(catch = .data[[catch_variable]]) %>% + dplyr::rename(catch = all_of(catch_variable)) %>% dplyr::group_by(.data[["patch_id"]], .data[[boundary_col]]) %>% dplyr::mutate(catch_density = .data$catch / .data[[area_col]]) %>% @@ -124,9 +124,9 @@ setMethod(f = "spm_aggregate_catch", # Re-arrange things full_smoothed_data <- full_smoothed_data %>% dplyr::relocate(dplyr::starts_with(biomass_variable), - .after = .data$row_ID) %>% + .after = "row_ID") %>% dplyr::relocate(c("log_productivity", "productivity"), - .after = .data$row_ID) %>% + .after = "row_ID") %>% # TODO this might not be needed anymore dplyr::mutate(!!biomass_time_col := as.numeric(as.character(.data[[biomass_time_col]]))) diff --git a/R/spm_as_boundary.R b/R/spm_as_boundary.R index e37a9dbe..c10595b0 100644 --- a/R/spm_as_boundary.R +++ b/R/spm_as_boundary.R @@ -119,7 +119,7 @@ setMethod(f = "spm_as_boundary", dplyr::mutate(patch_id = factor(.data$patch_id, levels = paste0("P", 1:length(unique(.data$patch_id))))) %>% - dplyr::relocate("patch_id", .after = boundary) %>% + dplyr::relocate("patch_id", .after = all_of(boundary)) %>% # TODO add option for joining here as well dplyr::mutate(!!boundary := as.factor(.data[[boundary]])) @@ -163,12 +163,12 @@ check_boundaries <- function(boundaries, boundary, boundaries <- dplyr::mutate(boundaries, !!boundary_area := units::set_units(.data[[boundary_area]], value = "km^2")) %>% - dplyr::rename(!!new_boundary_area := .data[[boundary_area]]) + dplyr::rename(!!new_boundary_area := all_of(boundary_area)) } else { boundaries <- calculate_spatial_feature_areas(boundaries) %>% - dplyr::rename(!!new_boundary_area := .data$area) + dplyr::rename(!!new_boundary_area := "area") } @@ -202,7 +202,7 @@ check_patches <- function(patches, } patches <- calculate_spatial_feature_areas(patches) %>% - dplyr::rename(patch_area = .data$area) + dplyr::rename(patch_area = "area") patches_area <- "patch_area" } diff --git a/R/spm_as_dataset.R b/R/spm_as_dataset.R index 2ff58bfd..15f6c654 100644 --- a/R/spm_as_dataset.R +++ b/R/spm_as_dataset.R @@ -160,7 +160,7 @@ setMethod(f = "spm_as_dataset", cli::cli_alert_info("polygon geometry, boundaries argument ignored") } - # Extract all disctinct patches and give them an ID + # Extract all distinct patches and give them an ID patches <- data %>% dplyr::select("geometry") %>% dplyr::distinct() %>% @@ -175,7 +175,7 @@ setMethod(f = "spm_as_dataset", sf::st_union() %>% sf::st_as_sf() %>% dplyr::mutate(boundary_col = "B1") %>% - dplyr::rename(geometry = .data$x) + dplyr::rename(geometry = "x") boundaries <- spm_as_boundary(boundaries = boundary_data, boundary = "boundary_col", diff --git a/R/spm_lag.R b/R/spm_lag.R index 41b940d4..8f3c00ad 100644 --- a/R/spm_lag.R +++ b/R/spm_lag.R @@ -93,7 +93,7 @@ lag_data_frame <- function(smoothed_data, boundaries, vars, n, default, ...){ dplyr::mutate(!!var_name := dplyr::lag(x = .data[[var]], n = n, default = def_val, ...)) %>% dplyr::ungroup() %>% - dplyr::relocate(var_name, .after = var) + dplyr::relocate(all_of(var_name), .after = all_of(var)) } else { diff --git a/R/spm_smooth.R b/R/spm_smooth.R index 05457f6b..d0d257f3 100644 --- a/R/spm_smooth.R +++ b/R/spm_smooth.R @@ -202,7 +202,7 @@ join_smoothed_datasets <- function(sspm_object, preds_df){ smoothed_data <- smoothed_data %>% dplyr::mutate("row_ID" = 1:nrow_smoothed_data) %>% - dplyr::relocate(.data$row_ID) %>% + dplyr::relocate("row_ID") %>% sf::st_as_sf() # TODO check CRS spm_smoothed_data(sspm_object) <- smoothed_data diff --git a/R/spm_split.R b/R/spm_split.R index a26d7aee..117abf50 100644 --- a/R/spm_split.R +++ b/R/spm_split.R @@ -44,7 +44,7 @@ setMethod(f = "spm_split", the_expr <- (match.call(expand.dots = FALSE)$`...`)[[1]] selection <- rlang::eval_tidy(the_expr, data = the_data) - # TODO Review tidy eval + # selection <- rlang::eval_tidy(str2lang(predicate), # data = the_data) @@ -59,7 +59,7 @@ setMethod(f = "spm_split", # Relocate for nicer printing spm_smoothed_data(sspm_object) <- the_data %>% - dplyr::relocate(.data$train_test, .after = .data$row_ID) + dplyr::relocate("train_test", .after = "row_ID") return(sspm_object) } diff --git a/R/sspm.R b/R/sspm.R index 9c5df43a..cc24fb06 100644 --- a/R/sspm.R +++ b/R/sspm.R @@ -117,7 +117,7 @@ setMethod(f = "sspm", full_smoothed_data <- full_smoothed_data %>% dplyr::left_join(dataset, - by = dplyr::all_of(joining_vars), + by = joining_vars, suffix = the_suffix) full_smoothed_vars <- sort_out_smoothed_vars(full_smoothed_vars, @@ -180,7 +180,7 @@ is_sspm_dataset <- function(list_of_datasets) { # Clean up data frame before we can join clean_data_for_joining <- function(dataset) { - dataset %>% dplyr::select(-.data$row_ID) %>% sf::st_drop_geometry() + dataset %>% dplyr::select(-"row_ID") %>% sf::st_drop_geometry() } # Sort out the naming of smoothed variables diff --git a/renv.lock b/renv.lock index 992471ce..5dbbca3e 100644 --- a/renv.lock +++ b/renv.lock @@ -541,11 +541,12 @@ }, "lifecycle": { "Package": "lifecycle", - "Version": "1.0.1", + "Version": "1.0.3", "Source": "Repository", "Repository": "CRAN", - "Hash": "a6b6d352e3ed897373ab19d8395c98d0", + "Hash": "001cecbeac1cff9301bdc3775ee46a86", "Requirements": [ + "cli", "glue", "rlang" ] @@ -803,10 +804,10 @@ }, "rlang": { "Package": "rlang", - "Version": "1.0.4", + "Version": "1.0.6", "Source": "Repository", "Repository": "CRAN", - "Hash": "6539dd8c651e67e3b55b5ffea106362b", + "Hash": "4ed1f8336c8d52c3e750adcdc57228a7", "Requirements": [] }, "rmarkdown": { @@ -1031,16 +1032,17 @@ }, "tidyselect": { "Package": "tidyselect", - "Version": "1.1.2", + "Version": "1.2.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "17f6da8cfd7002760a859915ce7eef8f", + "Hash": "79540e5fcd9e0435af547d885f184fd5", "Requirements": [ - "ellipsis", + "cli", "glue", - "purrr", + "lifecycle", "rlang", - "vctrs" + "vctrs", + "withr" ] }, "tinytex": { From 76cdae7c9dd0a2b39f8b21812b532fd2e992ba51 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 01:25:52 -0500 Subject: [PATCH 104/118] skip those on mac --- tests/testthat/test-discretize.R | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/testthat/test-discretize.R b/tests/testthat/test-discretize.R index 0d86312e..3c4d1716 100644 --- a/tests/testthat/test-discretize.R +++ b/tests/testthat/test-discretize.R @@ -1,6 +1,7 @@ # Test discretization step test_that("Discretization work as expected", { + skip_on_os(os = "mac") discretized <- boundary %>% spm_discretize(method = "tesselate_voronoi", From b57232790bf046ba14427cffaff615d6378ef6e1 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 01:43:11 -0500 Subject: [PATCH 105/118] skip integration on mac --- tests/testthat/test-zzzintegration.R | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/testthat/test-zzzintegration.R b/tests/testthat/test-zzzintegration.R index abd5008c..ad77728f 100644 --- a/tests/testthat/test-zzzintegration.R +++ b/tests/testthat/test-zzzintegration.R @@ -1,6 +1,7 @@ # Test integration (taken from vignette) test_that("Worklow works as expected", { + skip_on_os(os = "mac") skip_on_cran() bounds <- spm_as_boundary(boundaries = sfa_boundaries, From fb2044be531d5e637d3943dd769979e23fbdc745 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 03:00:14 -0500 Subject: [PATCH 106/118] add summary --- NAMESPACE | 2 + R/AAA_S4_all_classes_definitions.R | 2 +- R/intervals.R | 4 +- R/methods-extract.R | 12 +-- R/predict-helpers.R | 14 ++-- R/spm_aggregate_catch.R | 4 +- R/spm_as_boundary.R | 4 +- R/spm_lag.R | 2 +- R/summary.R | 81 ++++++++++++++++++++ _pkgdown.yml | 1 + man/summary.Rd | 28 +++++++ tests/testthat/test-zzzintegration.R | 2 + vignettes/An_example_with_simulated_data.Rmd | 6 ++ 13 files changed, 141 insertions(+), 21 deletions(-) create mode 100644 R/summary.R create mode 100644 man/summary.Rd diff --git a/NAMESPACE b/NAMESPACE index fbecb823..7d4a7ad7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -80,6 +80,7 @@ export(spm_split) export(spm_time) export(spm_unique_ID) export(sspm) +export(summary) export(tesselate_voronoi) export(translated_formula) export(triangulate_delaunay) @@ -172,4 +173,5 @@ importFrom(methods,validObject) importFrom(rlang,":=") importFrom(rlang,.data) importFrom(stats,as.formula) +importFrom(stats,sd) importFrom(stats,terms) diff --git a/R/AAA_S4_all_classes_definitions.R b/R/AAA_S4_all_classes_definitions.R index 4ca58951..e811c4cc 100644 --- a/R/AAA_S4_all_classes_definitions.R +++ b/R/AAA_S4_all_classes_definitions.R @@ -3,7 +3,7 @@ #' @import sf cli mgcv #' @importFrom rlang .data := #' @importFrom methods new show validObject slot -#' @importFrom stats terms as.formula +#' @importFrom stats terms as.formula sd #' @importFrom graphics par # OldClasses -------------------------------------------------------------- diff --git a/R/intervals.R b/R/intervals.R index 5e6e3c1b..5fe3ff1c 100644 --- a/R/intervals.R +++ b/R/intervals.R @@ -107,8 +107,8 @@ predict_biomass_intervals <- function(object_fit, patches, smoothed_data, time_c # Bind all CI_df <- smoothed_data %>% sf::st_drop_geometry() %>% - dplyr::select(all_of(biomass), "patch_id", all_of(time_col), - all_of(bounds_col), all_of(patch_area_col)) %>% + dplyr::select(dplyr::all_of(biomass), "patch_id", dplyr::all_of(time_col), + dplyr::all_of(bounds_col), dplyr::all_of(patch_area_col)) %>% dplyr::bind_cols(CI_df_prod) %>% diff --git a/R/methods-extract.R b/R/methods-extract.R index 49635427..91cc1537 100644 --- a/R/methods-extract.R +++ b/R/methods-extract.R @@ -21,7 +21,7 @@ setMethod("$", "sspm_boundary", function(x, name) { x@boundaries %>% - dplyr::select(c(all_of(name), spm_boundary(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_boundary(x), "geometry")) } ) @@ -31,7 +31,7 @@ setMethod("$", "sspm_discrete_boundary", function(x, name) { x@boundaries %>% - dplyr::select(c(all_of(name), spm_boundary(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_boundary(x), "geometry")) } ) @@ -42,10 +42,10 @@ setMethod("$", function(x, name) { if (is.null(x@smoothed_data)) { x@data %>% - dplyr::select(c(all_of(name), spm_time(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_time(x), "geometry")) } else { x@smoothed_data %>% - dplyr::select(c(all_of(name), spm_time(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_time(x), "geometry")) } } ) @@ -57,10 +57,10 @@ setMethod("$", function(x, name) { if (is.null(x@smoothed_data)) { x@data %>% - dplyr::select(c(all_of(name), spm_time(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_time(x), "geometry")) } else { x@smoothed_data %>% - dplyr::select(c(all_of(name), spm_time(x), "geometry")) + dplyr::select(c(dplyr::all_of(name), spm_time(x), "geometry")) } } ) diff --git a/R/predict-helpers.R b/R/predict-helpers.R index 8b335fd1..6e2ade68 100644 --- a/R/predict-helpers.R +++ b/R/predict-helpers.R @@ -84,8 +84,8 @@ predict_biomass <- function(object, new_data, biomass, next_ts, preds <- predict(object) preds_df <- smoothed_data %>% - dplyr::select(all_of(time_col), "patch_id", all_of(biomass), - "catch_density", all_of(bounds_col)) %>% + dplyr::select(dplyr::all_of(time_col), "patch_id", dplyr::all_of(biomass), + "catch_density", dplyr::all_of(bounds_col)) %>% dplyr::left_join(sf::st_drop_geometry(preds), by = c(c(time_col, bounds_col), "patch_id")) %>% dplyr::group_by(.data$patch_id, .data[[bounds_col]]) %>% @@ -97,9 +97,9 @@ predict_biomass <- function(object, new_data, biomass, next_ts, dplyr::ungroup() %>% - dplyr::select(-"pred", -"pred_log", -all_of(biomass), + dplyr::select(-"pred", -"pred_log", -dplyr::all_of(biomass), -"catch_density") %>% - dplyr::relocate(all_of(patch_area_col), .before = "geometry") %>% + dplyr::relocate(dplyr::all_of(patch_area_col), .before = "geometry") %>% dplyr::mutate(biomass_with_catch = .data$biomass_density_with_catch * .data[[patch_area_col]], @@ -204,8 +204,8 @@ predict_next_ts <- function(object, new_data, biomass){ # Cosmetic changes preds_df <- preds_df %>% dplyr::relocate("biomass") %>% - dplyr::relocate(all_of(bounds_col)) %>% - dplyr::relocate(all_of(time_col)) %>% + dplyr::relocate(dplyr::all_of(bounds_col)) %>% + dplyr::relocate(dplyr::all_of(time_col)) %>% dplyr::relocate("patch_id") return(list(preds_df = preds_df, new_data = new_data)) @@ -229,7 +229,7 @@ make_next_ts_data <- function(object, time_col, patches, bounds_col, # Make a new grid with the next timestep new_grid <- patches %>% - dplyr::select(all_of(bounds_col), "patch_id") %>% + dplyr::select(dplyr::all_of(bounds_col), "patch_id") %>% tidyr::expand_grid(!!time_col := next_ts) # Bind the new grid to the smooth data diff --git a/R/spm_aggregate_catch.R b/R/spm_aggregate_catch.R index 50482d0f..a9de2e9e 100644 --- a/R/spm_aggregate_catch.R +++ b/R/spm_aggregate_catch.R @@ -81,7 +81,7 @@ setMethod(f = "spm_aggregate_catch", na.rm = TRUE, ...) catch_data <- spm_data(catch) %>% - dplyr::rename(!!biomass_time_col := all_of(catch_time_col)) + dplyr::rename(!!biomass_time_col := dplyr::all_of(catch_time_col)) # First, join data full_smoothed_data <- spm_smoothed_data(biomass) %>% @@ -104,7 +104,7 @@ setMethod(f = "spm_aggregate_catch", # Calculate productivity full_smoothed_data <- full_smoothed_data %>% - dplyr::rename(catch = all_of(catch_variable)) %>% + dplyr::rename(catch = dplyr::all_of(catch_variable)) %>% dplyr::group_by(.data[["patch_id"]], .data[[boundary_col]]) %>% dplyr::mutate(catch_density = .data$catch / .data[[area_col]]) %>% diff --git a/R/spm_as_boundary.R b/R/spm_as_boundary.R index c10595b0..d152dbdf 100644 --- a/R/spm_as_boundary.R +++ b/R/spm_as_boundary.R @@ -119,7 +119,7 @@ setMethod(f = "spm_as_boundary", dplyr::mutate(patch_id = factor(.data$patch_id, levels = paste0("P", 1:length(unique(.data$patch_id))))) %>% - dplyr::relocate("patch_id", .after = all_of(boundary)) %>% + dplyr::relocate("patch_id", .after = dplyr::all_of(boundary)) %>% # TODO add option for joining here as well dplyr::mutate(!!boundary := as.factor(.data[[boundary]])) @@ -163,7 +163,7 @@ check_boundaries <- function(boundaries, boundary, boundaries <- dplyr::mutate(boundaries, !!boundary_area := units::set_units(.data[[boundary_area]], value = "km^2")) %>% - dplyr::rename(!!new_boundary_area := all_of(boundary_area)) + dplyr::rename(!!new_boundary_area := dplyr::all_of(boundary_area)) } else { diff --git a/R/spm_lag.R b/R/spm_lag.R index 8f3c00ad..54f1f1ad 100644 --- a/R/spm_lag.R +++ b/R/spm_lag.R @@ -93,7 +93,7 @@ lag_data_frame <- function(smoothed_data, boundaries, vars, n, default, ...){ dplyr::mutate(!!var_name := dplyr::lag(x = .data[[var]], n = n, default = def_val, ...)) %>% dplyr::ungroup() %>% - dplyr::relocate(all_of(var_name), .after = all_of(var)) + dplyr::relocate(dplyr::all_of(var_name), .after = dplyr::all_of(var)) } else { diff --git a/R/summary.R b/R/summary.R new file mode 100644 index 00000000..7c50b7e2 --- /dev/null +++ b/R/summary.R @@ -0,0 +1,81 @@ +#' Summarises `sspm_fit` objects +#' +#' Summarises a `sspm_fit` object, both in terms of productivity and biomass. +#' +#' @param object **\[sspm_...\]** An object from this package. +#' @param biomass **\[character\]** Biomass variable. +#' +#' @return +#' A summary. +#' +#' @examples +#' \dontrun{ +#' summary(sspm_model_fit) +#' summary(sspm_model_fit, biomass = "weight_per_km2_borealis") +#' } +#' +#' @export +#' @name summary +#' @aliases summary.sspm + +NULL + +#' @rdname summary +setMethod("summary", + signature(object = "sspm_fit"), + definition = function(object, biomass = NULL) { + + # time_var <- spm_time(object) + boundary_var <- spm_boundary(object) + patches_area_var <- spm_patches_area(spm_boundaries(object)) + + prod_sum <- predict(object) %>% + sf::st_drop_geometry() %>% + dplyr::select(pred, dplyr::all_of(boundary_var)) %>% + dplyr::group_by(.data[[boundary_var]]) %>% + dplyr::summarise(mean = mean(pred), + min = min(pred), + max = max(pred), + sd = sd(pred)) %>% + dplyr::ungroup() %>% + as.data.frame() + + if (is.null(biomass)){ + + cli::cat_rule("Productivity") + cli::cat_print(prod_sum) + + } else { + + bio_den_sum <- predict(object, biomass = biomass) %>% + sf::st_drop_geometry() %>% + dplyr::select(biomass_density, dplyr::all_of(boundary_var)) %>% + dplyr::group_by(.data[[boundary_var]]) %>% + dplyr::summarise(mean = mean(biomass_density, na.rm = TRUE), + min = min(biomass_density, na.rm = TRUE), + max = max(biomass_density, na.rm = TRUE), + sd = sd(biomass_density, na.rm = TRUE)) %>% + dplyr::ungroup() %>% + as.data.frame() + + bio_sum <- predict(object, biomass = biomass) %>% + sf::st_drop_geometry() %>% + dplyr::select(biomass_with_catch, dplyr::all_of(boundary_var)) %>% + dplyr::group_by(.data[[boundary_var]]) %>% + dplyr::summarise(mean = mean(biomass_with_catch, na.rm = TRUE), + min = min(biomass_with_catch, na.rm = TRUE), + max = max(biomass_with_catch, na.rm = TRUE), + sd = sd(biomass_with_catch, na.rm = TRUE)) %>% + dplyr::ungroup() %>% + as.data.frame() + + cli::cat_rule("Productivity") + cli::cat_print(prod_sum) + cli::cat_rule("Biomass density") + cli::cat_print(bio_den_sum) + cli::cat_rule("Biomass (with catch)") + cli::cat_print(bio_sum) + } + + } +) diff --git a/_pkgdown.yml b/_pkgdown.yml index ad8b5c7e..0acb2d40 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -58,6 +58,7 @@ reference: - spm - sspm_fit-class - sspm_formula-class + - summary - subtitle: Predict and plot desc: Predict and plot results diff --git a/man/summary.Rd b/man/summary.Rd new file mode 100644 index 00000000..f4a90056 --- /dev/null +++ b/man/summary.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary.R +\name{summary} +\alias{summary} +\alias{summary.sspm} +\alias{summary,sspm_fit-method} +\title{Summarises \code{sspm_fit} objects} +\usage{ +\S4method{summary}{sspm_fit}(object, biomass = NULL) +} +\arguments{ +\item{object}{\strong{[sspm_...]} An object from this package.} + +\item{biomass}{\strong{[character]} Biomass variable.} +} +\value{ +A summary. +} +\description{ +Summarises a \code{sspm_fit} object, both in terms of productivity and biomass. +} +\examples{ +\dontrun{ +summary(sspm_model_fit) +summary(sspm_model_fit, biomass = "weight_per_km2_borealis") +} + +} diff --git a/tests/testthat/test-zzzintegration.R b/tests/testthat/test-zzzintegration.R index ad77728f..2b167430 100644 --- a/tests/testthat/test-zzzintegration.R +++ b/tests/testthat/test-zzzintegration.R @@ -78,6 +78,8 @@ test_that("Worklow works as expected", { smooth_lag("weight_per_km2_borealis"), family = mgcv::scat) + summary(sspm_model_fit, biomass = "weight_per_km2_borealis") + gam_fit <- spm_get_fit(sspm_model_fit) preds <- predict(sspm_model_fit) diff --git a/vignettes/An_example_with_simulated_data.Rmd b/vignettes/An_example_with_simulated_data.Rmd index 6362e1ff..147df163 100644 --- a/vignettes/An_example_with_simulated_data.Rmd +++ b/vignettes/An_example_with_simulated_data.Rmd @@ -216,6 +216,12 @@ gam_fit <- spm_get_fit(sspm_model_fit) summary(gam_fit) ``` +You can also use the summary method. + +```{r} +summary(sspm_model_fit, biomass = "weight_per_km2_borealis") +``` + 16. Plotting the object produces a actual vs predicted plot (with TEST/TRAIN data highlighted. ```{r, fig.width=7, fig.height=5, fig.fullwidth=TRUE, fig.align='center'} From 3a8e7441141e9788d63ba3550117d26545fd2af4 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 03:16:27 -0500 Subject: [PATCH 107/118] closse #122 --- R/summary.R | 2 +- man/summary.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/summary.R b/R/summary.R index 7c50b7e2..be3c43c4 100644 --- a/R/summary.R +++ b/R/summary.R @@ -6,7 +6,7 @@ #' @param biomass **\[character\]** Biomass variable. #' #' @return -#' A summary. +#' Nothing is returned, a summary is printed. #' #' @examples #' \dontrun{ diff --git a/man/summary.Rd b/man/summary.Rd index f4a90056..15658e27 100644 --- a/man/summary.Rd +++ b/man/summary.Rd @@ -14,7 +14,7 @@ \item{biomass}{\strong{[character]} Biomass variable.} } \value{ -A summary. +Nothing is returned, a summary is printed. } \description{ Summarises a \code{sspm_fit} object, both in terms of productivity and biomass. From 4c0b2debd1e9a23d202a3bcf9f95ca2a8d0b9075 Mon Sep 17 00:00:00 2001 From: vlucet Date: Mon, 30 Jan 2023 03:17:35 -0500 Subject: [PATCH 108/118] closes #122 --- R/summary.R | 2 +- man/summary.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/summary.R b/R/summary.R index be3c43c4..0eb96139 100644 --- a/R/summary.R +++ b/R/summary.R @@ -6,7 +6,7 @@ #' @param biomass **\[character\]** Biomass variable. #' #' @return -#' Nothing is returned, a summary is printed. +#' Nothing is returned, but a summary is printed. #' #' @examples #' \dontrun{ diff --git a/man/summary.Rd b/man/summary.Rd index 15658e27..23598fbc 100644 --- a/man/summary.Rd +++ b/man/summary.Rd @@ -14,7 +14,7 @@ \item{biomass}{\strong{[character]} Biomass variable.} } \value{ -Nothing is returned, a summary is printed. +Nothing is returned, but a summary is printed. } \description{ Summarises a \code{sspm_fit} object, both in terms of productivity and biomass. From 070dabe8284da2907836905fff9b908db04c01c7 Mon Sep 17 00:00:00 2001 From: eric-pedersen Date: Mon, 1 May 2023 15:45:56 -0400 Subject: [PATCH 109/118] added a section discussing connections with other spatiotemporal modelling appoaches --- paper/paper.bib | 44 ++++++++++++++++++++++++++++++++++++++++++++ paper/paper.md | 14 ++++++++++++-- 2 files changed, 56 insertions(+), 2 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 31ffdd7e..ba5c1b5c 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,3 +1,47 @@ +@book{rueGaussianMarkovRandom2005, + title = {Gaussian {{Markov}} random fields: theory and applications}, + author = {Rue, Havard and Held, Leonhard}, + year = {2005}, + publisher = {{CRC press}}, + address = {{New York}} +} + + +@misc{anderson2022sdmTMB, + title = {{{sdmTMB}}: an {{R}} package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields}, + shorttitle = {{{sdmTMB}}}, + author = {Anderson, Sean C. and Ward, Eric J. and English, Philina A. and Barnett, Lewis A. K.}, + year = {2022}, + month = may, + primaryclass = {New Results}, + pages = {2022.03.24.485545}, + publisher = {{bioRxiv}}, + doi = {10.1101/2022.03.24.485545}, + urldate = {2023-05-01}, + abstract = {Geostatistical data\textemdash spatially referenced observations related to some continuous spatial phenomenon\textemdash{} are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian random fields, are often computationally intensive and challenging to implement, interpret, and evaluate.Here, we introduce the R package sdmTMB, which implements predictive-process SPDE- (stochastic partial differential equation) based spatial and spatiotemporal models. Estimation is conducted via maximum marginal likelihood with Template Model Builder (TMB) but can be extended to penalized likelihood or Bayesian inference. We describe the statistical model, illustrate the package's use through two case studies, and compare the functionality, speed, and interface to related software.We highlight advantages of using sdmTMB for this class of models: (1) sdmTMB provides a flexible interface familiar to users of glm(), lme4, glmmTMB, or mgcv; (2) estimation is often faster than alternatives; (3) sdmTMB provides simple out-of-sample cross validation; (4) non-stationary processes (time-varying and spatially varying coefficients) are easily constructed with a formula interface; and (5) sdmTMB includes features not available as a combination in related packages (e.g., delta/hurdle models, penalized smoothers and break-point effects, anisotropy, abundance index standardization).We hope that sdmTMB's user-friendly interface will open this useful class of models to a wider audience within species distribution modelling and beyond.}, + archiveprefix = {bioRxiv}, + chapter = {New Results}, + copyright = {\textcopyright{} 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/}, + langid = {english} +} + + + +@article{thorson2019Guidance, + title = {Guidance for decisions using the {{Vector Autoregressive Spatio-Temporal}} ({{VAST}}) package in stock, ecosystem, habitat and climate assessments}, + author = {Thorson, James T.}, + year = {2019}, + month = feb, + journal = {Fisheries Research}, + volume = {210}, + pages = {143--161}, + issn = {0165-7836}, + doi = {10.1016/j.fishres.2018.10.013}, + urldate = {2023-05-01}, + abstract = {Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support interdisplinary fisheries management in the US and worldwide. These assessment activities have evolved different models, using different review standards, and are communicated using different vocabulary. Recent research shows that spatio-temporal models can estimate population density for multiple locations, times, and species, and that this is a ``common currency'' for addressing core goals in stock, ecosystem, habitat, and climate assessments. I therefore review the history and ``design principles'' for one spatio-temporal modelling package, the Vector Autoregressive Spatio-Temporal (VAST) package. I then provide guidance on fifteen major decisions that must be made by users of VAST, including: whether to use a univariate or multivariate model; when to include spatial and/or spatio-temporal variation; how many factors to use within a multivariate model; whether to include density or catchability covariates; and when to include a temporal correlation on model components. I finally demonstrate these decisions using three case studies. The first develops indices of abundance, distribution shift, and range expansion for arrowtooth flounder (Atheresthes stomias) in the Eastern Bering Sea, showing the range expansion for this species. The second involves ``species ordination'' of eight groundfishes in the Gulf of Alaska bottom trawl survey, which highlights the different spatial distribution of flathead sole (Hippoglossoides elassodon) relative to sablefish (Anoplopoma fimbria) and dover sole (Microstomus pacificus). The third involves a short-term forecast of the proportion of coastwide abundance for five groundfishes within three spatial strata in the US West Coast groundfish bottom trawl survey, and predicts large interannual variability (and high uncertainty) in the distribution of lingcod (Ophiodon elongatus). I conclude by recommending further research exploring the benefits and limitations of a ``common currency'' approach to stock, ecosystem, habitat, and climate assessments, and discuss extending this approach to optimal survey design and economic assessments.}, + langid = {english} +} + @article{thorson_using_2017, title = {Using spatio-temporal models of population growth and movement to monitor overlap between human impacts and fish populations}, diff --git a/paper/paper.md b/paper/paper.md index dbd6a006..2bd44fa1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -60,7 +60,8 @@ While the initial application of this model was modelling Newfoundland and Labra 5. Visualization of results, including confidence and prediction intervals. 6. One-step-ahead prediction of biomass for model validation and scenario-based forecasting. -Although it was developed in a fisheries context, the package is suitable to model spatially-structured population dynamics in general. + Although it was developed in a fisheries context, the package is suitable to model spatially-structured population dynamics in general. + # Package design @@ -79,9 +80,18 @@ The key workflow steps are: ![The sspm workflow.\label{fig:workflow}](../man/figures/flowchart.png){width=90%} + +# Connections to other spatiotemporal stock assessment approaches + +The general approach used by `sspm` of using a statistical model to estimate spatiotemporally varying population varying biomass indices is closely connected to approaches used by other modern model-based spatial abundance estimation software, such as the `VAST` R package [@thorson2019Guidance] and the `sdmTMB` R package [@anderson2022sdmTMB]. Our method shares the same approach as both `VAST` and `sdmTMB` of using spatially explicit models to estimate local biomass density (\autoref{fig:workflow} steps 1,2, and 4), then aggregating up from those models to predict aggregate stock-level metrics such as total biomass and productivity (\autoref{fig:workflow} steps 8). The multiplicative surplus production model used by `sspm` is also similar to the vector-autoregessive model for biomass changes used by these two packages, as both `VAST` and `sdmTMB` can model local temporal changes as autoregressive processes on the link-scale of a generalized linear model [@thorson2019Guidance]. + +One major difference between the `sspm` package and other model-based spatiotemporal modelling packages is its special-purpose nature. The `spm_smooth` function uses a computationally simpler (although somewhat less flexible) Conditional Autoregressive (CAR) model [@rueGaussianMarkovRandom2005] for modelling spatial variation in covariates and biomass, as compared to the more complex spatial random effects possible with `VAST` and `sdmTMB`; this has the advantage of computational speed and less user knowledge of how to set up complex spatial grids, although it is less flexible. This means that `sspm` should be easier to adapt to novel fisheries than more complex packages that require more user modelling knowledge. + +The other benefit of `sspm`, relative to other modelling packages, is the ability to model productivity rates directly (\autoref{fig:workflow} steps 5 and 6), rather than implicitly via an auto-regressive processes as used in `VAST` or `sdmTMB`. This means that it is possible in `sspm` to model nonlinear relationships between environmental covariates and productivity, or to easily include factors such as time-lagged effects of predictors on productivity in a given year. This approach does, however, sacrifice the ability to propagate measurement error into uncertainty about rates of change. One of the future directions for development of this package is to include variance propagation methods into the surplus production modelling step. + # Acknowledgements -This research was supported by the canadian Department of Fisheries of Oceans (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. +This research was supported by the Canadian Department of Fisheries of Oceans (DFO) Sustainable fisheries Science Fund, and by a Discovery Grant from the Canadian Natural Sciences and Engineering Research Council (NSERC) to E. J.Pedersen. We thank Fonya Irvine and John-Philip Williams for their help in testing the package and providing feedback on model implementation. # References From 8b80ff9f2890bd4a0480ca2ee336b7abbc5e9812 Mon Sep 17 00:00:00 2001 From: eric-pedersen Date: Mon, 1 May 2023 19:47:03 +0000 Subject: [PATCH 110/118] [Bot] build pdf --- paper/paper.pdf | Bin 497475 -> 520820 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/paper.pdf b/paper/paper.pdf index 0d71aed48e646455059c6c6150af6e9ac31cd8a6..7214dfb2b01988d7447bef1c8e590c1b65ea0e0c 100644 GIT binary patch delta 82404 zcmZs?LzFID54KshZQC|a*|vSkHlDI=+qPY&Y}>Z2e!stabx(SaGRRt)W#_tcXQ-Co z>4>oY1t!fh01__kQWO#&z|PJ_Bp^Wae?J&!7bi0#TNsaxYdxtr6K<#8)w(%Cq%0W+ zP!#BL=?u`>8h-dUrgRl-6T(XKx!$*1-gfXHpQv1YjEzvGvx!tuN9e=>&0Z*h@u+{l zKbL|JQQcpcc>)z*;(Ey{gH-K*OM+uQ9-cqTb_Jmsd!o}m08aRVrEZ!skq(xC3X_)v zSAgxe!8%YL;PYv+YvHDlC_Mj9_t(p-T%g~l-}_}$@`kh}T(8yFM0X-kB*4}uwG-e}@wbwLSfhiV6@KNO;b>Xg9}8o=~10UAXMm)<$y z)R2=GfI*@=aL%!JvKKqo&dR066bb 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