diff --git a/.Rbuildignore b/.Rbuildignore index 6fdf189..f2cc340 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,12 +1,11 @@ -^renv$ -^renv\.lock$ ^.*\.Rproj$ ^\.Rproj\.user$ -^\.travis\.yml$ -^README\.Rmd$ -^README\.md$ -^NEWS\.md$ -^README-.*\.png$ -^_pkgdown.yml -^docs/ -^docker/ +^renv$ +^renv\.lock$ +README.Rmd +Makefile +LICENSE +^\.github$ +_pkgdown.yml +docs/ +docker/ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 0000000..2d19fc7 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/Features.yaml b/.github/workflows/Features.yaml new file mode 100644 index 0000000..c2be57d --- /dev/null +++ b/.github/workflows/Features.yaml @@ -0,0 +1,73 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [feat/**] + paths: [DESCRIPTION, .Rbuildignore, R/**, tests/**, inst/**, .github/**] + + +name: Feature-branch-checks + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: true + matrix: + config: + - {os: ubuntu-latest, r: 'release'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: rcmdcheck, covr, tinytest, lintr, pkgdown + + # - name: Lint + # run: | + # library(tinytest) + # out <- lintr::lint_dir() + # if (0 < length(out)) stop("Linting failure") else print("Linting successful") + # shell: Rscript {0} + + - name: Coverage + run: | + library(tinytest) + roxygen2::roxygenize() + test_covr <- covr::percent_coverage(covr::package_coverage()) + if (test_covr < 90) stop(sprintf("Coverage of %s (<90) too low", lowest_covr_value)) + shell: Rscript {0} + + - name: Check + env: + _R_CHECK_CRAN_INCOMING_: false + run: | + options(crayon.enabled = TRUE) + rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + + - name: Check if pkgdown site can be build + run: make pkgdown + shell: bash + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@main + with: + name: ${{ runner.os }}-r${{ matrix.config.r }}-results + path: check diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml new file mode 100644 index 0000000..64a0a5c --- /dev/null +++ b/.github/workflows/R-CMD-check.yaml @@ -0,0 +1,60 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [master, development] + paths: [DESCRIPTION, .Rbuildignore, R/**, tests/**, inst/**, .github/**] + pull_request: + branches: [master, development] + paths: [DESCRIPTION, .Rbuildignore, R/**, tests/**, inst/**, .github/**] + +name: R-CMD-check + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: macOS-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: rcmdcheck + + - name: Check + env: + _R_CHECK_CRAN_INCOMING_: false + run: | + options(crayon.enabled = TRUE) + roxygen2::roxygenize() + rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@main + with: + name: ${{ runner.os }}-r${{ matrix.config.r }}-results + path: check diff --git a/.github/workflows/Test-coverage.yaml b/.github/workflows/Test-coverage.yaml new file mode 100644 index 0000000..385c291 --- /dev/null +++ b/.github/workflows/Test-coverage.yaml @@ -0,0 +1,46 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [master, development] + paths: [DESCRIPTION, .Rbuildignore, R/**, tests/**, inst/**, .github/**] + +name: Test coverage + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: true + matrix: + config: + - {os: ubuntu-latest, r: 'release'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: covr, tinytest + + - name: Coverage + run: | + library(tinytest) + roxygen2::roxygenize() + covr::codecov() + shell: Rscript {0} diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..2d6e98a --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,38 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [development] + +name: pkgdown + +jobs: + pkgdown: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.5.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.gitignore b/.gitignore index 7ccd857..89576df 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,47 @@ -.Rproj.user +# History files .Rhistory +.Rapp.history + +# Session Data files .RData -.Rprofile -inst/doc -renv/ + +# User-specific files +.Ruserdata + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md + +# R Environment Variables +.Renviron + +# Roxygen generated files +NAMESPACE +man/ + +# pkgdown generated files +docs/ +/docker/.env diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 2c99855..0000000 --- a/.travis.yml +++ /dev/null @@ -1,16 +0,0 @@ -# https://docs.travis-ci.com/user/languages/r/ - -language: r -sudo: false -warnings_are_errors: false - -r: -- oldrel -- release -- devel - -r_packages: - - covr - -after_success: -- Rscript -e 'covr::codecov()' diff --git a/DESCRIPTION b/DESCRIPTION index c5aea01..3562423 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: simTool Type: Package Title: Conduct Simulation Studies with a Minimal Amount of Source Code -Version: 1.1.7 +Version: 1.1.8 Authors@R: person("Marsel", "Scheer", role = c("aut", "cre"), email = "scheer@freescience.de") Maintainer: Marsel Scheer Description: Tool for statistical simulations that have two components. @@ -28,9 +28,12 @@ Suggests: boot, broom, rmarkdown, - tinytest + tinytest, + lintr, + roxygen2, + covr License: GPL-3 VignetteBuilder: knitr -RoxygenNote: 7.1.1 -URL: https://github.com/MarselScheer/simTool +RoxygenNote: 7.3.2 +URL: https://marselscheer.github.io/simTool/ BugReports: https://github.com/MarselScheer/simTool/issues diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..fd44df8 --- /dev/null +++ b/Makefile @@ -0,0 +1,41 @@ +SHELL := /bin/bash +PKGNAME=simTool + +help: + -@ echo "R-cmd-check: Builds and checks (--as-cran) the Rpkg" + -@ echo "test: Executes all unit-tests" + -@ echo "coverage: Calculates the test coverage of the unit-test" + -@ echo "lint: Starts linting" + -@ echo "README: Builds README.md" + -@ echo "pkgdown: Builds pkgdown site" + +NAMESPACE: R/* + Rscript -e "roxygen2::roxygenize()" + +R-cmd-check: NAMESPACE + Rscript -e "devtools::check(document=FALSE, args=c('--as-cran'))" + # R CMD build . + # R CMD check --as-cran --no-manual $(PKGNAME)*.tar.gz + # make clean-pkg-build-file + # make clean-cmd-check-files + +clean-pkg-build-file: + rm $(PKGNAME)*tar.gz + +clean-cmd-check-files: + rm -rf $(PKGNAME).Rcheck + +test: NAMESPACE + Rscript -e "pkgload::load_all(); tinytest::test_all()" + +coverage: NAMESPACE + Rscript -e "pkgload::load_all(); covr::package_coverage(type = 'tests')" + +lint: + Rscript -e "library(tinytest); pkgload::load_all(); lintr::lint_package()" + +pkgdown: NAMESPACE + Rscript -e "library(pkgdown); pkgdown::build_site()" + +README: + Rscript -e "pkgload::load_all(); rmarkdown::render(input='README.Rmd', output_format='md_document')" diff --git a/NEWS.md b/NEWS.md index dd69417..4b19ee0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ + +Version 1.1.8 +========================= + +* Upcoming R-version does not allow to apply attr() to primitives to assign values. Fix this by decorating primitives if they are used. + Version 1.1.7 ========================= diff --git a/R/expand_tibble.R b/R/expand_tibble.R index 241dc12..831cdc2 100644 --- a/R/expand_tibble.R +++ b/R/expand_tibble.R @@ -7,7 +7,7 @@ #' @param \dots vectors, factors or a list containing these. #' @return See \code{\link{expand.grid}} but instead of a #' \code{\link{data.frame}} -#' a \code{\link{tibble}} is returned. +#' a \code{\link[tibble:tibble]{tibble}} is returned. #' @author Marsel Scheer #' @seealso \code{\link{expand.grid}} #' @examples diff --git a/R/extract_fun_and_parameter.R b/R/extract_fun_and_parameter.R index 3ba195d..2d8c053 100644 --- a/R/extract_fun_and_parameter.R +++ b/R/extract_fun_and_parameter.R @@ -2,8 +2,17 @@ extract_fun_and_parameter <- function(row, envir) { idx_na <- which(is.na(row)) idx_gt <- which(names(row) == ".truth") + copy_primitives <- function(fun) { + # upcoming R-version will not support attr(fun, "xyz") <- sth if fun is + # a builtin function like mean, min, max, ... + # so we wrap it to have a non-primitive function + if (!is.primitive(fun)) { + return(fun) + } + return(function(...) fun(...)) + } ret <- list( - fun = get(unlist(row[1, 1]), envir = envir), + fun = copy_primitives(fun=get(unlist(row[1, 1]), envir = envir)), para = as.list(row[1, -c(1, idx_na, idx_gt), drop = FALSE]) ) diff --git a/R/print.eval_tibbles.R b/R/print.eval_tibbles.R index 8764486..953d0b2 100644 --- a/R/print.eval_tibbles.R +++ b/R/print.eval_tibbles.R @@ -7,6 +7,7 @@ #' @param ... not used. only necessary to define the function consistently #' with respect to \code{print(x, ...)} #' @author Marsel Scheer +#' @keywords internal #' @export print.eval_tibbles <- function(x, ...) { print(x$simulation) diff --git a/README.Rmd b/README.Rmd index 5bb879f..b019cdf 100644 --- a/README.Rmd +++ b/README.Rmd @@ -12,23 +12,18 @@ knitr::opts_chunk$set( ) ``` -```{r badges, results='asis', echo=FALSE} -suppressPackageStartupMessages(library(badgecreatr)) -acc <- "MarselScheer" -repo <- "simTool" -branch <- "master" -status <- "active" -lifecycle <- "Stable" -badges <- list( - badge_travis(ghaccount = acc, ghrepo = repo, branch = branch), - badge_lifecycle(lifecycle = lifecycle), - badge_projectstatus(status = status), - badge_cran_version_ago(packagename = repo), - badge_cran_downloads(packagename = repo), - badge_license() - ) -cat(paste(badges, collapse = " ")) -``` + +[![R build status](https://github.com/MarselScheer/simTool/workflows/R-CMD-check/badge.svg)](https://github.com/MarselScheer/simTool/actions) +[![Coverage Status](https://img.shields.io/codecov/c/github/MarselScheer/simTool/develop.svg)](https://codecov.io/github/MarselScheer/simTool?branch=develop) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) +[![Project Status: Active – The project has reached a stable, usable +state and is being actively +developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/simTool)](https://cran.r-project.org/package=simTool) +[![metacran +downloads](https://cranlogs.r-pkg.org/badges/simTool)](https://cran.r-project.org/package=simTool) +[![license](https://img.shields.io/badge/license-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html) + # simTool @@ -71,9 +66,7 @@ et You can install simTool from github with: ```{r gh-installation, eval = FALSE} -install.packages("simTool") -# install.packages("devtools") -devtools::install_github("MarselScheer/simTool") +remotes::install_github("MarselScheer/simTool") ``` Or from CRAN with: diff --git a/README.md b/README.md index 4706dfe..d1ee4b9 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,12 @@ - -[![Build -Status](https://travis-ci.org/MarselScheer/simTool.svg?branch=master)](https://travis-ci.org/MarselScheer/simTool) -[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable) +[![R build +status](https://github.com/MarselScheer/simTool/workflows/R-CMD-check/badge.svg)](https://github.com/MarselScheer/simTool/actions) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![CRAN\_Status\_Badge\_version\_ago](https://www.r-pkg.org/badges/version-ago/simTool)](https://cran.r-project.org/package=simTool) +[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/simTool)](https://cran.r-project.org/package=simTool) [![metacran downloads](https://cranlogs.r-pkg.org/badges/simTool)](https://cran.r-project.org/package=simTool) [![license](https://img.shields.io/badge/license-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html) @@ -35,63 +34,52 @@ random variables of size 10, 50, 100, and 1000. Afterwards the *t.test* using confidence levels 0.8, 0.9, 0.95 are applied. This is repeated 1000 times to estimate the coverage: -``` r -library(simTool) -dg <- expand_tibble(fun = "rexp", rate = 10, n = c(10L, 50L, 100L, 1000L)) -pg <- expand_tibble(proc = "t.test", conf.level = c(0.8, 0.9, 0.95)) -et <- eval_tibbles(dg, pg, - ncpus = 4, - replications = 10^3, - post_analyze = function(ttest) tibble::tibble( - coverage = ttest$conf.int[1] <= 1 / 10 && 1 / 10 <= ttest$conf.int[2]), - summary_fun = list(mean = mean) -) -#> | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% -et -#> # A tibble: 12 x 8 -#> fun rate n replications summary_fun proc conf.level coverage -#> -#> 1 rexp 10 10 1 mean t.test 0.8 0.754 -#> 2 rexp 10 10 1 mean t.test 0.9 0.855 -#> 3 rexp 10 10 1 mean t.test 0.95 0.905 -#> 4 rexp 10 50 1 mean t.test 0.8 0.808 -#> 5 rexp 10 50 1 mean t.test 0.9 0.905 -#> 6 rexp 10 50 1 mean t.test 0.95 0.945 -#> 7 rexp 10 100 1 mean t.test 0.8 0.792 -#> 8 rexp 10 100 1 mean t.test 0.9 0.895 -#> 9 rexp 10 100 1 mean t.test 0.95 0.936 -#> 10 rexp 10 1000 1 mean t.test 0.8 0.796 -#> 11 rexp 10 1000 1 mean t.test 0.9 0.897 -#> 12 rexp 10 1000 1 mean t.test 0.95 0.953 -#> Number of data generating functions: 4 -#> Number of analyzing procedures: 3 -#> Number of replications: 1000 -#> Estimated replications per hour: 614008 -#> Start of the simulation: 2020-09-16 19:53:05 -#> End of the simulation: 2020-09-16 19:53:11 -``` + library(simTool) + dg <- expand_tibble(fun = "rexp", rate = 10, n = c(10L, 50L, 100L, 1000L)) + pg <- expand_tibble(proc = "t.test", conf.level = c(0.8, 0.9, 0.95)) + et <- eval_tibbles(dg, pg, + ncpus = 4, + replications = 10^3, + post_analyze = function(ttest) tibble::tibble( + coverage = ttest$conf.int[1] <= 1 / 10 && 1 / 10 <= ttest$conf.int[2]), + summary_fun = list(mean = mean) + ) + et + #> # A tibble: 12 × 8 + #> fun rate n replications summary_fun proc conf.level coverage + #> + #> 1 rexp 10 10 1 mean t.test 0.8 0.754 + #> 2 rexp 10 10 1 mean t.test 0.9 0.855 + #> 3 rexp 10 10 1 mean t.test 0.95 0.905 + #> 4 rexp 10 50 1 mean t.test 0.8 0.808 + #> 5 rexp 10 50 1 mean t.test 0.9 0.905 + #> 6 rexp 10 50 1 mean t.test 0.95 0.945 + #> 7 rexp 10 100 1 mean t.test 0.8 0.792 + #> 8 rexp 10 100 1 mean t.test 0.9 0.895 + #> 9 rexp 10 100 1 mean t.test 0.95 0.936 + #> 10 rexp 10 1000 1 mean t.test 0.8 0.796 + #> 11 rexp 10 1000 1 mean t.test 0.9 0.897 + #> 12 rexp 10 1000 1 mean t.test 0.95 0.953 + #> Number of data generating functions: 4 + #> Number of analyzing procedures: 3 + #> Number of replications: 1000 + #> Estimated replications per hour: 1318457 + #> Start of the simulation: 2025-04-05 05:37:59.800157 + #> End of the simulation: 2025-04-05 05:38:02.530619 ## Installation You can install simTool from github with: -``` r -install.packages("simTool") -# install.packages("devtools") -devtools::install_github("MarselScheer/simTool") -``` + remotes::install_github("MarselScheer/simTool") Or from CRAN with: -``` r -install.packages("simTool") -``` + install.packages("simTool") ## Test suite With tinytest you can rerun the test for simTool by calling -``` r -# install.packages("tinytest") -tinytest::test_package("simTool") -``` + # install.packages("tinytest") + tinytest::test_package("simTool") diff --git a/_pkgdown.yml b/_pkgdown.yml index cd40c23..1f27537 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,6 +1,7 @@ title: simTool url: http://MarselScheer.github.io/simTool template: + bootstrap: 5 params: bootswatch: flatly diff --git a/docker/Dockerfile b/docker/Dockerfile index df259a9..4e6690f 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,21 +1,30 @@ -FROM rocker/verse:4.0.2 +FROM rocker/verse:4.1.0 RUN apt-get update && apt-get install -y \ ranger \ git \ tig \ + htop \ emacs \ +# graphviz necessary for dot-programm for instance used +# by plantuml + graphviz \ # xdg-utils and ff necessary to display -# drake-network-graphs from doom-emacs +# drake-network-graphs from emacs xdg-utils \ firefox \ - && ln -sf /usr/share/zoneinfo/Europe/Berlin /etc/localtime \ - && git config --global user.email "scheer@freescience.de" \ - && git config --global user.name "Marsel Scheer" \ - && mv /root/.gitconfig /home/rstudio \ - && chown rstudio:rstudio /home/rstudio/.gitconfig \ - && usermod --shell /bin/bash rstudio \ -# create directory for mounting the host file system - && su -l rstudio -c "mkdir -p /tmp/hostfs" \ - && su -l rstudio -c "ln -s /tmp/hostfs /home/rstudio/hostfs" \ - && su -l rstudio -c "echo RENV_PATHS_ROOT=~/hostfs/renv/ > /home/rstudio/.Renviron" + && ln -sf /usr/share/zoneinfo/Europe/Berlin /etc/localtime + +USER rstudio +WORKDIR /home/rstudio +RUN mkdir -p /tmp/hostfs \ + && ln -s /tmp/hostfs \ + && ln -s /tmp/hostfs/.emacs.d \ + && ln -s /tmp/hostfs/.gitconfig \ + && ln -s /tmp/hostfs/.ssh \ + && echo "export DISPLAY=:0" > /home/rstudio/.bashrc \ + && echo RENV_PATHS_ROOT=~/hostfs/renv/ > /home/rstudio/.Renviron + +# last command MUST be USER root?!? +# otherwise container will directly stop +USER root diff --git a/docker/Dockerfile.doom b/docker/Dockerfile.doom deleted file mode 100644 index 53102c2..0000000 --- a/docker/Dockerfile.doom +++ /dev/null @@ -1,38 +0,0 @@ -FROM rstudio:4.0.2 - -USER rstudio - -# install and configure doom-emacs -RUN git clone --depth 1 https://github.com/hlissner/doom-emacs ~/.emacs.d \ - && yes | ~/.emacs.d/bin/doom install \ - && sed -i 's/;;ess/ess/g' ~/.doom.d/init.el \ - && ~/.emacs.d/bin/doom sync - -RUN sed -i "s/display-line-numbers-type t/display-line-numbers-type 'relative/g" ~/.doom.d/config.el \ - && sed -i "s/(setq doom-theme 'doom-one)/(setq doom-theme 'doom-peacock)/g" ~/.doom.d/config.el \ - && echo '(setq-default evil-escape-key-sequence "kj")' >> ~/.doom.d/config.el \ - && echo '(setq confirm-kill-emacs nil)' >> ~/.doom.d/config.el \ -# have no idea why this is the only way -# to add company-dabbrev-code to the -# backend. Just follow https://github.com/emacs-ess/ESS/issues/955 - && echo "(require 'company-dabbrev-code)\n\ -(add-to-list 'company-backends #'company-dabbrev-code)\n\ -(setq company-idle-delay 0)\n\ -(setq company-show-numbers t)\n\ -(after! ess-r-mode\n\ - (set-company-backend! 'ess-r-mode\n\ - '(:separate company-R-library company-R-args company-R-objects company-dabbrev-code)))" >> ~/.doom.d/config.el \ -# set general behaviour like indentation and evaluation - && echo "(after! ess-r-mode\n\ - (progn\n\ - (setq-default ess-style 'RStudio-)\n\ - (setq ess-eval-visibly 't)\n\ - )\n\ - )" >> ~/.doom.d/config.el \ -# set doom key-bindings -## save during insert mode - && echo "(map! :i \"C-x C-s\" #'save-buffer)" >> ~/.doom.d/config.el - -# root at the end is need, otherwise -# container stops directly after start?!? -USER root diff --git a/docker/Makefile b/docker/Makefile new file mode 100644 index 0000000..df2ca58 --- /dev/null +++ b/docker/Makefile @@ -0,0 +1,50 @@ +SHELL := /bin/bash +.ONESHELL: + +PARENT_NAME := sim-tool#$(shell basename $(dir $(PWD))) + +.PHONY: .env +.env: + @echo -------------------- $@ $$(date) -------------------- + echo "PARENT_NAME=$(PARENT_NAME)" > .env + +build-docker-context: + @echo -------------------- $@ $$(date) -------------------- + -rm -rf docker_context + mkdir docker_context + cp -r ide docker_context + echo + echo "Docker context created" + +build-docker: build-docker-context .env + @echo -------------------- $@ $$(date) -------------------- + sudo docker compose -f docker-compose.yaml -p $(PARENT_NAME) build || { exit 1; } + rm -rf docker_context + echo + echo "Docker image build" + +start-docker: .env + @echo -------------------- $@ $$(date) -------------------- + sudo docker compose -f docker-compose.yaml -p $(PARENT_NAME) up -d || { exit 1; } + echo + echo "Docker container is running..." + +shell-docker: + @echo -------------------- $@ $$(date) -------------------- + sudo docker exec -u rstudio -it ide /bin/sh -c "/bin/bash" || { exit 1; } + echo + echo "Docker container is running..." + +root-docker: + @echo -------------------- $@ $$(date) -------------------- + sudo docker exec -u root -it ide /bin/sh -c "/bin/bash" || { exit 1; } + echo + echo "Docker container is running..." + +stop-docker: + @echo -------------------- $@ $$(date) -------------------- + sudo docker compose -f docker-compose.yaml -p $(PARENT_NAME) stop || { exit 1; } + echo + echo "Docker container is stopped" + +rebuild-docker: stop-docker build-docker start-docker diff --git a/docker/build.sh b/docker/build.sh deleted file mode 100755 index 311c86f..0000000 --- a/docker/build.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -sudo docker build -t rstudio:4.0.2 . diff --git a/docker/build_doom.sh b/docker/build_doom.sh deleted file mode 100755 index 5b9fb79..0000000 --- a/docker/build_doom.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash - -./build.sh -sudo docker build -t doom_r:4.0.2 -f Dockerfile.doom . diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml new file mode 100644 index 0000000..d867a53 --- /dev/null +++ b/docker/docker-compose.yaml @@ -0,0 +1,38 @@ +services: + ide: + container_name: ide + build: + context: ./docker_context + dockerfile: ide/Dockerfile + args: + DOCKER_BUILDKIT: 1 + USER_ID: 1000 + GROUP_ID: 1000 + BASE_IMAGE: rocker/verse:4 + shm_size: 8gb + volumes: + - /home/m/docker_fs/repos/simTool:/home/rstudio/simTool + - /home/m/docker_fs/repos/orgfiles/:/home/rstudio/docker_fs/repos/orgfiles/ + - /home/m/docker_fs/dots/.emacs.d.lean:/home/rstudio/.emacs.d + - /home/m/docker_fs/dots/.ssh:/home/rstudio/.ssh + - /tmp/.X11-unix:/tmp/.X11-unix + environment: + - DISPLAY=:0 + - RUNNING_HOST=local + - DISABLE_AUTH=true + ports: + - 8787:8787 + ollama: + container_name: ollama + image: ollama/ollama:0.6.4 + ports: + - 11434:11434 + volumes: + - /home/m/docker_fs/ollama-cache:/root/.ollama + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] diff --git a/docker/ide/Dockerfile b/docker/ide/Dockerfile new file mode 100644 index 0000000..da007fa --- /dev/null +++ b/docker/ide/Dockerfile @@ -0,0 +1,31 @@ +ARG BASE_IMAGE= +FROM ${BASE_IMAGE} + +ARG USER_ID= +ARG GROUP_ID= + +RUN apt-get update && apt-get install -y \ + ranger \ + git \ + tig \ + htop \ + emacs \ +# graphviz necessary for dot-programm for instance used +# by plantuml + graphviz \ +# xdg-utils and ff necessary to display +# drake-network-graphs from emacs + xdg-utils \ + firefox \ + && ln -sf /usr/share/zoneinfo/Europe/Berlin /etc/localtime + +USER rstudio +WORKDIR /home/rstudio + +RUN git config --global user.email "scheer@freescience.de" \ + && git config --global user.name "Marsel Scheer" +# last command MUST be USER root?!? +# otherwise container RStudio is not available +USER root + + diff --git a/docker/start.sh b/docker/start.sh deleted file mode 100755 index 1aeabde..0000000 --- a/docker/start.sh +++ /dev/null @@ -1,5 +0,0 @@ -sudo docker run --rm -d -p 8787:8787 -e DISABLE_AUTH=true \ - -v ~/docker_fs:/tmp/hostfs \ - -v /tmp/.X11-unix:/tmp/.X11-unix \ - rstudio:4.0.2 - diff --git a/docker/start_doom.sh b/docker/start_doom.sh deleted file mode 100755 index 1b2e4a1..0000000 --- a/docker/start_doom.sh +++ /dev/null @@ -1,15 +0,0 @@ -sudo docker run --rm -d -p 8787:8787 -e DISABLE_AUTH=true \ - -v ~/docker_fs:/tmp/hostfs \ - -v /tmp/.X11-unix:/tmp/.X11-unix \ - doom_r:4.0.2 - -# in order to display doom-emacs on the host: -# echo $DISPLAY -# xhost +local:'containerID' -# docker exec -it containerID /bin/bash -# su -l rstudio -# export DISPLAY= -# emacs - - - diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index e1c703d..0000000 --- a/docs/404.html +++ /dev/null @@ -1,151 +0,0 @@ - - - - - - - - -Page not found (404) • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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The simTool package: Facilitate simulations
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Developed by Marsel Scheer.

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- - - - - - - - diff --git a/docs/articles/simTool.html b/docs/articles/simTool.html deleted file mode 100644 index 2c4af71..0000000 --- a/docs/articles/simTool.html +++ /dev/null @@ -1,941 +0,0 @@ - - - - - - - -The simTool package: Facilitate simulations • simTool - - - - - - - - - - -
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The simTool package: Facilitate simulations

-

Marsel Scheer

- -

2020-09-23

- - Source: vignettes/simTool.Rmd - - -
- - - - -
-

-A first example

-
    -
  1. Generate data
    2. -
  2. Fit different models
    3. -
  3. Repeat step 1 and 2 three times
    4. -
  4. Summarize the results with respect to the different data generating functions and models by calculating mean and standard deviation for the corresponding model terms
    5. -
-
regData <- function(n, SD) {
-  x <- seq(0, 1, length = n)
-  y <- 10 + 2 * x + rnorm(n, sd = SD)
-  tibble(x = x, y = y)
-}
-
-eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  post_analyze = broom::tidy,
-  summary_fun = list(mean = mean, sd = sd),
-  group_for_summary = "term",
-  replications = 3
-)
-
# A tibble: 16 x 12
-   fun       n    SD replications summary_fun proc  formula term  estimate std.error statistic
-   <chr> <int> <int>        <int> <chr>       <chr> <chr>   <chr>    <dbl>     <dbl>     <dbl>
- 1 regD…     5     1            1 mean        lm    y~x     (Int…   10.1      0.692     16.5  
- 2 regD…     5     1            1 mean        lm    y~x     x        2.92     1.13       3.59 
- 3 regD…     5     1            1 mean        lm    y~I(x^… (Int…   10.5      0.544     20.2  
- 4 regD…     5     1            1 mean        lm    y~I(x^… I(x^…    2.71     1.04       3.20 
- 5 regD…     5     1            1 sd          lm    y~x     (Int…    1.45     0.288      6.89 
- 6 regD…     5     1            1 sd          lm    y~x     x        1.84     0.471      3.71 
- 7 regD…     5     1            1 sd          lm    y~I(x^… (Int…    1.32     0.185      4.38 
- 8 regD…     5     1            1 sd          lm    y~I(x^… I(x^…    2.15     0.352      2.62 
- 9 regD…     5     2            1 mean        lm    y~x     (Int…    9.86     1.26       7.85 
-10 regD…     5     2            1 mean        lm    y~x     x        3.42     2.05       1.66 
-11 regD…     5     2            1 mean        lm    y~I(x^… (Int…   10.6      1.18       8.99 
-12 regD…     5     2            1 mean        lm    y~I(x^… I(x^…    2.65     2.24       1.17 
-13 regD…     5     2            1 sd          lm    y~x     (Int…    0.209    0.0374     0.393
-14 regD…     5     2            1 sd          lm    y~x     x        1.17     0.0610     0.515
-15 regD…     5     2            1 sd          lm    y~I(x^… (Int…    0.128    0.0488     0.436
-16 regD…     5     2            1 sd          lm    y~I(x^… I(x^…    1.32     0.0927     0.542
-# … with 1 more variable: p.value <dbl>
-Number of data generating functions: 2
-Number of analyzing procedures: 2
-Number of replications: 3
-Estimated replications per hour: 66584
-Start of the simulation: 2020-09-23 18:15:30
-End of the simulation: 2020-09-23 18:15:30
-
-
-

-Introduction

-

The purpose of the simTool package is to disengage the research from any kind of administrative source code which is usually an annoying necessity of a simulation study.

-

This vignette will give an introduction into the simTool package mainly by examples of growing complexity. The workhorse is the function eval_tibbles. Every parameter of this function will be discussed briefly and the functionality is illustrated by at least one example.

-
-
-

-Workflow

-

The workflow is quite easy and natural. One defines two data.frames (or tibbles), the first one represents the functions that generate the data sets and the second one represents the functions that analyze the data. These two data.frames are passed to eval_tibbles which conducts the simulation. Afterwards, the results can nicely be displayed as a data.frame.

-
-
-

-Defining the data.frames for data generation and analysis

-

There are 3 rules:

-
    -
  • the first column ( a character vector) defines the functions to be called
    • -
  • the other columns are the parameters that are passed to function specified in the first column
    • -
  • The entry NA will not be passed to the function specified in the first column.
    • -
-

The function expand_tibble is a convenient function for defining such data.frames.

-

We now define the data generation functions for our first simulation.

-
print(dg <- dplyr::bind_rows(
-  expand_tibble(fun = "rexp", n = c(10L, 20L), rate = 1:2),
-  expand_tibble(fun = "rnorm", n = c(10L, 20L), mean = 1:2)
-))
-
# A tibble: 8 x 4
-  fun       n  rate  mean
-  <chr> <int> <int> <int>
-1 rexp     10     1    NA
-2 rexp     20     1    NA
-3 rexp     10     2    NA
-4 rexp     20     2    NA
-5 rnorm    10    NA     1
-6 rnorm    20    NA     1
-7 rnorm    10    NA     2
-8 rnorm    20    NA     2
-

This data.frame represents 8 R-functions. For instance, the second row represents a function that generates 20 exponential distributed random variables with rate 1. Since mean=NA in the second row, this parameter is not passed to rexp.

-

Similar, we define the data.frame for data analyzing functions.

-
print(pg <- dplyr::bind_rows(
-  expand_tibble(proc = "min"),
-  expand_tibble(proc = "mean", trim = c(0.1, 0.2))
-))
-
# A tibble: 3 x 2
-  proc   trim
-  <chr> <dbl>
-1 min    NA  
-2 mean    0.1
-3 mean    0.2
-

Hence, this data.frame represents 3 R-functions i.e. calculating the minimum and the arithmetic mean with trim=0.1 and trim=0.2.

-
-
-

-The workhorse eval_tibbles -

-

The workhorse eval_tibbles has the following simplified pseudo code:

-
1.  convert dg to R-functions  {g_1, ..., g_k} 
-2.  convert pg to R-functions  {f_1, ..., f_L} 
-3.  initialize result object 
-4.  append dg and pg to the result object 
-5.  t1 = current.time() 
-6.  for g in  {g_1, ..., g_k} 
-7.      for r in 1:replications (optionally in a parallel manner) 
-8.          data = g() 
-9.          for f in  {f_1, \ldots, f_L} 
-10.             append f(data) to the result object (optionally apply a post-analyze-function)
-11.         optionally append data to the result object 
-12.      optionally summarize the result object over all  
-         replications but separately for f_1, ..., f_L (and optional group variables)
-13. t2 = current.time() 
-14. Estimate the number of replications per hour from t1 and t2
-

The object returned by eval_tibbles is a list of class eval_tibbles.

-
dg <- expand_tibble(fun = "rnorm", n = 10, mean = 1:2)
-pg <- expand_tibble(proc = "min")
-eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 2)
-eg
-
# A tibble: 4 x 6
-  fun       n  mean replications proc  results
-  <chr> <dbl> <int>        <int> <chr>   <dbl>
-1 rnorm    10     1            1 min    -1.75 
-2 rnorm    10     1            2 min    -0.212
-3 rnorm    10     2            1 min     0.232
-4 rnorm    10     2            2 min     1.16 
-Number of data generating functions: 2
-Number of analyzing procedures: 1
-Number of replications: 2
-Estimated replications per hour: 51446318
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-

As you can see, the function always estimates the number of replications that can be done in one hour.

-
-

-Parameter replications -

-

Of course, this parameter controls the number of replications conducted.

-
eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 3)
-eg
-
# A tibble: 6 x 6
-  fun       n  mean replications proc  results
-  <chr> <dbl> <int>        <int> <chr>   <dbl>
-1 rnorm    10     1            1 min    -1.30 
-2 rnorm    10     1            2 min    -0.574
-3 rnorm    10     1            3 min     0.503
-4 rnorm    10     2            1 min     0.729
-5 rnorm    10     2            2 min     1.06 
-6 rnorm    10     2            3 min    -0.272
-Number of data generating functions: 2
-Number of analyzing procedures: 1
-Number of replications: 3
-Estimated replications per hour: 56693971
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-
-
-

-Parameter discard_generated_data -

-

eval_tibbles saves ALL generated data sets.

-
eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 1)
-eg$simulation
-
# A tibble: 2 x 6
-  fun       n  mean replications proc   results
-  <chr> <dbl> <int>        <int> <chr>    <dbl>
-1 rnorm    10     1            1 min   -0.554  
-2 rnorm    10     2            1 min    0.00499
-
eg$generated_data
-
[[1]]
- [1]  1.04382239 -0.02157836  1.79342772  1.50162446 -0.55447035  1.88349784  0.79223220  2.83137401
- [9]  1.17726438  0.19415935
-
-[[2]]
- [1] 3.947932470 2.121197052 1.645050873 3.052866250 3.163113962 1.201167182 2.775455294 0.004992648
- [9] 1.649437807 2.380864742
-

In general, it is sometimes very handy to have the data sets in order to investigate unusual or unexpected results. But saving the generated data sets can be very memory consuming. Stop saving the generated data sets can be obtained by setting discardGeneratedData = TRUE. See command line 11 in the pseudo code.

-
-
-

-Parameter summary_fun -

-

As stated in command line 12 we can summarize the result objects over all replications but separately for all data analyzing functions.

-
dg <- expand_tibble(fun = "runif", n = c(10, 20, 30))
-pg <- expand_tibble(proc = c("min", "max"))
-eval_tibbles(
-  data_grid = dg, proc_grid = pg, replications = 1000,
-  summary_fun = list(mean = mean)
-)
-
# A tibble: 6 x 6
-  fun       n replications summary_fun proc   value
-  <chr> <dbl>        <int> <chr>       <chr>  <dbl>
-1 runif    10            1 mean        min   0.0904
-2 runif    10            1 mean        max   0.910 
-3 runif    20            1 mean        min   0.0486
-4 runif    20            1 mean        max   0.953 
-5 runif    30            1 mean        min   0.0314
-6 runif    30            1 mean        max   0.967 
-Number of data generating functions: 3
-Number of analyzing procedures: 2
-Number of replications: 1000
-Estimated replications per hour: 22712598
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-
eval_tibbles(
-  data_grid = dg, proc_grid = pg, replications = 1000,
-  summary_fun = list(mean = mean, sd = sd)
-)
-
# A tibble: 12 x 6
-   fun       n replications summary_fun proc   value
-   <chr> <dbl>        <int> <chr>       <chr>  <dbl>
- 1 runif    10            1 mean        min   0.0930
- 2 runif    10            1 mean        max   0.907 
- 3 runif    10            1 sd          min   0.0865
- 4 runif    10            1 sd          max   0.0860
- 5 runif    20            1 mean        min   0.0463
- 6 runif    20            1 mean        max   0.952 
- 7 runif    20            1 sd          min   0.0425
- 8 runif    20            1 sd          max   0.0468
- 9 runif    30            1 mean        min   0.0325
-10 runif    30            1 mean        max   0.966 
-11 runif    30            1 sd          min   0.0323
-12 runif    30            1 sd          max   0.0311
-Number of data generating functions: 3
-Number of analyzing procedures: 2
-Number of replications: 1000
-Estimated replications per hour: 22522507
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-

Note, by specifying the parameter summary_fun the generated data sets and all individual result objects are discarded. In this example we discard \(3 \times 1000\) data sets and \(3 \times 1000 \times 2\) individual result objects.

-
-
-

-Parameter post_analyze -

-

Sometimes the analyzing functions return quite complicated objects like in the Section A first example.

-
eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  replications = 2
-)
-
# A tibble: 8 x 7
-  fun         n    SD replications proc  formula  results
-  <chr>   <int> <int>        <int> <chr> <chr>    <list> 
-1 regData     5     1            1 lm    y~x      <lm>   
-2 regData     5     1            1 lm    y~I(x^2) <lm>   
-3 regData     5     1            2 lm    y~x      <lm>   
-4 regData     5     1            2 lm    y~I(x^2) <lm>   
-5 regData     5     2            1 lm    y~x      <lm>   
-6 regData     5     2            1 lm    y~I(x^2) <lm>   
-7 regData     5     2            2 lm    y~x      <lm>   
-8 regData     5     2            2 lm    y~I(x^2) <lm>   
-Number of data generating functions: 2
-Number of analyzing procedures: 2
-Number of replications: 2
-Estimated replications per hour: 481596
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-

The parameter post_analyze (if specified) is applied directly after the result was generated (see command line 10). Note, purrr::compose can be very handy if your post-analyzing-function can be defined by a few single functions:

-
eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  post_analyze = purrr::compose(function(mat) mat["(Intercept)", "Estimate"], coef, summary.lm),
-  replications = 2
-)
-
# A tibble: 8 x 7
-  fun         n    SD replications proc  formula  results
-  <chr>   <int> <int>        <int> <chr> <chr>      <dbl>
-1 regData     5     1            1 lm    y~x        10.3 
-2 regData     5     1            1 lm    y~I(x^2)   10.2 
-3 regData     5     1            2 lm    y~x         9.43
-4 regData     5     1            2 lm    y~I(x^2)    9.95
-5 regData     5     2            1 lm    y~x        13.5 
-6 regData     5     2            1 lm    y~I(x^2)   13.3 
-7 regData     5     2            2 lm    y~x         9.27
-8 regData     5     2            2 lm    y~I(x^2)    9.25
-Number of data generating functions: 2
-Number of analyzing procedures: 2
-Number of replications: 2
-Estimated replications per hour: 332602
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:31
-
-
-

-Parameter group_for_summary -

-

When the result object is a data.frame itself, for instance

-
presever_rownames <- function(mat) {
-  rn <- rownames(mat)
-  ret <- tibble::as_tibble(mat)
-  ret$term <- rn
-  ret
-}
-
-eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  post_analyze = purrr::compose(presever_rownames, coef, summary),
-  replications = 3
-)
-
# A tibble: 24 x 11
-   fun        n    SD replications proc  formula  Estimate `Std. Error` `t value` `Pr(>|t|)` term   
-   <chr>  <int> <int>        <int> <chr> <chr>       <dbl>        <dbl>     <dbl>      <dbl> <chr>  
- 1 regDa…     5     1            1 lm    y~x          9.91        0.670    14.8     0.000671 (Inter…
- 2 regDa…     5     1            1 lm    y~x          2.80        1.09      2.56    0.0831   x      
- 3 regDa…     5     1            1 lm    y~I(x^2)    10.3         0.577    17.9     0.000381 (Inter…
- 4 regDa…     5     1            1 lm    y~I(x^2)     2.63        1.10      2.40    0.0961   I(x^2) 
- 5 regDa…     5     1            2 lm    y~x          9.32        0.537    17.3     0.000419 (Inter…
- 6 regDa…     5     1            2 lm    y~x          2.11        0.878     2.40    0.0960   x      
- 7 regDa…     5     1            2 lm    y~I(x^2)     9.78        0.590    16.6     0.000478 (Inter…
- 8 regDa…     5     1            2 lm    y~I(x^2)     1.56        1.12      1.39    0.259    I(x^2) 
- 9 regDa…     5     1            3 lm    y~x          9.64        1.16      8.28    0.00369  (Inter…
-10 regDa…     5     1            3 lm    y~x          1.83        1.90      0.965   0.406    x      
-# … with 14 more rows
-Number of data generating functions: 2
-Number of analyzing procedures: 2
-Number of replications: 3
-Estimated replications per hour: 338682
-Start of the simulation: 2020-09-23 18:15:31
-End of the simulation: 2020-09-23 18:15:32
-

In order to summarize the replications it is necessary to additional group the calculations with respect to another variable. This variable can be passed to group_for_summary

-
eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  post_analyze = purrr::compose(presever_rownames, coef, summary),
-  summary_fun = list(mean = mean, sd = sd),
-  group_for_summary = "term",
-  replications = 3
-)
-
# A tibble: 16 x 12
-   fun       n    SD replications summary_fun proc  formula term  Estimate `Std. Error` `t value`
-   <chr> <int> <int>        <int> <chr>       <chr> <chr>   <chr>    <dbl>        <dbl>     <dbl>
- 1 regD…     5     1            1 mean        lm    y~x     (Int…    9.95         0.500     53.5 
- 2 regD…     5     1            1 mean        lm    y~x     x        2.04         0.817      6.86
- 3 regD…     5     1            1 mean        lm    y~I(x^… (Int…   10.3          0.444     28.5 
- 4 regD…     5     1            1 mean        lm    y~I(x^… I(x^…    1.93         0.845      2.80
- 5 regD…     5     1            1 sd          lm    y~x     (Int…    0.505        0.404     66.9 
- 6 regD…     5     1            1 sd          lm    y~x     x        0.382        0.660      8.87
- 7 regD…     5     1            1 sd          lm    y~I(x^… (Int…    0.608        0.202     17.8 
- 8 regD…     5     1            1 sd          lm    y~I(x^… I(x^…    0.759        0.385      1.87
- 9 regD…     5     2            1 mean        lm    y~x     (Int…    9.38         0.787     13.6 
-10 regD…     5     2            1 mean        lm    y~x     x        3.28         1.28       2.80
-11 regD…     5     2            1 mean        lm    y~I(x^… (Int…    9.99         0.823     14.1 
-12 regD…     5     2            1 mean        lm    y~I(x^… I(x^…    2.74         1.57       1.79
-13 regD…     5     2            1 sd          lm    y~x     (Int…    1.06         0.271      7.18
-14 regD…     5     2            1 sd          lm    y~x     x        3.14         0.443      2.41
-15 regD…     5     2            1 sd          lm    y~I(x^… (Int…    0.862        0.365      6.91
-16 regD…     5     2            1 sd          lm    y~I(x^… I(x^…    2.74         0.694      1.73
-# … with 1 more variable: `Pr(>|t|)` <dbl>
-Number of data generating functions: 2
-Number of analyzing procedures: 2
-Number of replications: 3
-Estimated replications per hour: 118069
-Start of the simulation: 2020-09-23 18:15:32
-End of the simulation: 2020-09-23 18:15:32
-
-
-

-Parameter ncpus and cluster_seed -

-

By specifying ncpus larger than 1 a cluster objected is created for the user and passed to the parameter cluster discussed in the next section.

-
eval_tibbles(
-  data_grid = dg, proc_grid = pg, replications = 10,
-  ncpus = 2, summary_fun = list(mean = mean)
-)
-
# A tibble: 6 x 6
-  fun       n replications summary_fun proc   value
-  <chr> <dbl>        <int> <chr>       <chr>  <dbl>
-1 runif    10            1 mean        min   0.0884
-2 runif    10            1 mean        max   0.912 
-3 runif    20            1 mean        min   0.0656
-4 runif    20            1 mean        max   0.947 
-5 runif    30            1 mean        min   0.0362
-6 runif    30            1 mean        max   0.969 
-Number of data generating functions: 3
-Number of analyzing procedures: 2
-Number of replications: 10
-Estimated replications per hour: 16571
-Start of the simulation: 2020-09-23 18:15:32
-End of the simulation: 2020-09-23 18:15:34
-

As it is stated in command line 7, the replications are parallelized. In our case, this means that roughly every CPU conducts 5 replications.

-

The parameter cluster_seed must be an integer vector of length 6 and serves the same purpose as the function set.seed. By default, cluster_seed equals rep(12345, 6). Note, in order to reproduce the simulation study it is also necessary that ncpus does not change.

-
-
-

-Parameter cluster -

-

The user can create a cluster on its own. This also enables the user to distribute the replications over different computers in a network.

-
library(parallel)
-cl <- makeCluster(rep("localhost", 2), type = "PSOCK")
-eval_tibbles(
-  data_grid = dg, proc_grid = pg, replications = 10,
-  cluster = cl, summary_fun = list(mean = mean)
-)
-
# A tibble: 6 x 6
-  fun       n replications summary_fun proc   value
-  <chr> <dbl>        <int> <chr>       <chr>  <dbl>
-1 runif    10            1 mean        min   0.0884
-2 runif    10            1 mean        max   0.912 
-3 runif    20            1 mean        min   0.0656
-4 runif    20            1 mean        max   0.947 
-5 runif    30            1 mean        min   0.0362
-6 runif    30            1 mean        max   0.969 
-Number of data generating functions: 3
-Number of analyzing procedures: 2
-Number of replications: 10
-Estimated replications per hour: 74036
-Start of the simulation: 2020-09-23 18:15:34
-End of the simulation: 2020-09-23 18:15:35
- -

As you can see our cluster consists of 3 workers. Hence, this reproduces the results from the last code chunk above. Further note, if the user starts the cluster, the user also has to stop the cluster. A cluster that is created within eval_tibbles by specifying ncpus is also stop within eval_tibbles.

-
-
-

-Parameter cluster_libraries and cluster_global_objects -

-

A newly created cluster is ``empty’’. Hence, if the simulation study requires libraries or objects from the global environment, they must be transferred to the cluster.

-

Lets look at standard example from the boot package.

-
library(boot)
-ratio <- function(d, w) sum(d$x * w) / sum(d$u * w)
-city.boot <- boot(city, ratio,
-  R = 999, stype = "w",
-  sim = "ordinary"
-)
-boot.ci(city.boot,
-  conf = c(0.90, 0.95),
-  type = c("norm", "basic", "perc", "bca")
-)
-
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
-Based on 999 bootstrap replicates
-
-CALL : 
-boot.ci(boot.out = city.boot, conf = c(0.9, 0.95), type = c("norm", 
-    "basic", "perc", "bca"))
-
-Intervals : 
-Level      Normal              Basic         
-90%   ( 1.098,  1.861 )   ( 1.047,  1.772 )   
-95%   ( 1.026,  1.934 )   ( 0.860,  1.799 )  
-
-Level     Percentile            BCa          
-90%   ( 1.268,  1.994 )   ( 1.271,  2.002 )   
-95%   ( 1.241,  2.180 )   ( 1.242,  2.195 )  
-Calculations and Intervals on Original Scale
-

The following data generating function is extremely boring because it always returns the data set city from the library boot.

-
returnCity <- function() {
-  city
-}
-bootConfInt <- function(data) {
-  city.boot <- boot(data, ratio,
-    R = 999, stype = "w",
-    sim = "ordinary"
-  )
-  boot.ci(city.boot,
-    conf = c(0.90, 0.95),
-    type = c("norm", "basic", "perc", "bca")
-  )
-}
-

The function ratio exists at the moment only in our global environment. Further we had to load the boot package. Hence, we load the boot package by setting cluster_libraries = c(“boot”) and transfer the function ratio by setting cluster_global_objects = c(“ratio”).

-
dg <- expand_tibble(fun = "returnCity")
-pg <- expand_tibble(proc = "bootConfInt")
-eval_tibbles(dg, pg,
-  replications = 10, ncpus = 2,
-  cluster_libraries = c("boot"),
-  cluster_global_objects = c("ratio")
-)
-
# A tibble: 10 x 4
-   fun        replications proc        results 
-   <chr>             <int> <chr>       <list>  
- 1 returnCity            1 bootConfInt <bootci>
- 2 returnCity            2 bootConfInt <bootci>
- 3 returnCity            3 bootConfInt <bootci>
- 4 returnCity            4 bootConfInt <bootci>
- 5 returnCity            5 bootConfInt <bootci>
- 6 returnCity            6 bootConfInt <bootci>
- 7 returnCity            7 bootConfInt <bootci>
- 8 returnCity            8 bootConfInt <bootci>
- 9 returnCity            9 bootConfInt <bootci>
-10 returnCity           10 bootConfInt <bootci>
-Number of data generating functions: 1
-Number of analyzing procedures: 1
-Number of replications: 10
-Estimated replications per hour: 152858
-Start of the simulation: 2020-09-23 18:15:36
-End of the simulation: 2020-09-23 18:15:36
-

Of course, it is possible to set cluster_global_objects=ls(), but then all objects from the global environment are transferred to all workers.

-
-
-

-Parameter envir -

-

The function eval_tibbles generates in a first step function calls from data_grid and proc_grid. This is achieved by applying the R-function get. By default, envir=globalenv() and thus get searches the global environment of the current R session. An example shows how to use the parameter envir.

-
# masking summary from the base package
-summary <- function(x) tibble(sd = sd(x))
-g <- function(x) tibble(q0.1 = quantile(x, 0.1))
-someFunc <- function() {
-  summary <- function(x) tibble(sd = sd(x), mean = mean(x))
-
-  dg <- expand_tibble(fun = "runif", n = 100)
-  pg <- expand_tibble(proc = c("summary", "g"))
-
-  # the standard is to use the global
-  # environment, hence summary defined outside
-  # of someFunc() will be used
-  print(eval_tibbles(dg, pg))
-  cat("--------------------------------------------------\n")
-  # will use the local defined summary, but g
-  # from the global environment, because
-  # g is not locally defined.
-  print(eval_tibbles(dg, pg, envir = environment()))
-}
-someFunc()
-
# A tibble: 2 x 6
-  fun       n replications proc        sd   q0.1
-  <chr> <dbl>        <int> <chr>    <dbl>  <dbl>
-1 runif   100            1 summary  0.260 NA    
-2 runif   100            1 g       NA      0.123
-Number of data generating functions: 1
-Number of analyzing procedures: 2
-Number of replications: 1
-Estimated replications per hour: 1662024
-Start of the simulation: 2020-09-23 18:15:36
-End of the simulation: 2020-09-23 18:15:36
---------------------------------------------------
-# A tibble: 2 x 7
-  fun       n replications proc        sd   mean   q0.1
-  <chr> <dbl>        <int> <chr>    <dbl>  <dbl>  <dbl>
-1 runif   100            1 summary  0.282  0.512 NA    
-2 runif   100            1 g       NA     NA      0.147
-Number of data generating functions: 1
-Number of analyzing procedures: 2
-Number of replications: 1
-Estimated replications per hour: 1910119
-Start of the simulation: 2020-09-23 18:15:36
-End of the simulation: 2020-09-23 18:15:36
-
-
-

-.truth-functionality

-

Sometimes it is handy to access the parameter constellation that was used during the data generation in the (post) data analyzing phase. Of course, one could write wrapper functions for every data generating function and append the parameter constellation from the data generation as attributes to the data set, but the purpose of this package is to reduce such administrative source code. Hence if the (post) data analyzing function has an argument .truth, then eval_tibbles will manage that hand-over. A brief example should explain this. Suppose we want to estimate the bias of the empirical quantile estimator if the data is normal distributed.

-
dg <- expand_tibble(fun = c("rnorm"), mean = c(1,1000), sd = c(1,10), n = c(10L, 100L))
-pg <- expand_tibble(proc = "quantile", probs = 0.975)
-post_ana <- function(q_est, .truth){
-  tibble::tibble(bias = q_est - stats::qnorm(0.975, mean = .truth$mean, sd = .truth$sd))
-}
-eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
-                   ncpus = 2,
-                   post_analyze = post_ana,
-                   summary_fun = list(mean = mean))
-
# A tibble: 8 x 9
-  fun    mean    sd     n replications summary_fun proc     probs    bias
-  <chr> <dbl> <dbl> <int>        <int> <chr>       <chr>    <dbl>   <dbl>
-1 rnorm     1     1    10            1 mean        quantile 0.975 -0.573 
-2 rnorm  1000     1    10            1 mean        quantile 0.975 -0.550 
-3 rnorm     1    10    10            1 mean        quantile 0.975 -5.58  
-4 rnorm  1000    10    10            1 mean        quantile 0.975 -5.34  
-5 rnorm     1     1   100            1 mean        quantile 0.975 -0.0760
-6 rnorm  1000     1   100            1 mean        quantile 0.975 -0.0879
-7 rnorm     1    10   100            1 mean        quantile 0.975 -0.685 
-8 rnorm  1000    10   100            1 mean        quantile 0.975 -0.695 
-Number of data generating functions: 8
-Number of analyzing procedures: 1
-Number of replications: 1000
-Estimated replications per hour: 848756
-Start of the simulation: 2020-09-23 18:15:36
-End of the simulation: 2020-09-23 18:15:41
-

If we want to do the analysis for different distrubtions we could modify our post data analyzing function, but we can also simply add a .truth-column to the data generating grid. In this case, the information from the .truth-column is directly passed to the .truth-parameter:

-
dg <- dplyr::bind_rows(
-  expand_tibble(fun = c("rnorm"), mean = 0, n = c(10L, 100L), .truth = qnorm(0.975)),
-  expand_tibble(fun = c("rexp"), rate = 1, n = c(10L, 100L), .truth = qexp(0.975, rate = 1)),
-  expand_tibble(fun = c("runif"), max = 2, n = c(10L, 100L), .truth = qunif(0.975, max = 2))
-)
-pg <- expand_tibble(proc = "quantile", probs = 0.975)
-post_ana <- function(q_est, .truth){
-  ret <- q_est - .truth
-  names(ret) <- "bias"
-  ret
-}
-eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
-                   ncpus = 2,
-                   post_analyze = post_ana,
-                   summary_fun = list(mean = mean))
-
# A tibble: 6 x 11
-  fun    mean     n .truth  rate   max replications summary_fun proc     probs    bias
-  <chr> <dbl> <int>  <dbl> <dbl> <dbl>        <int> <chr>       <chr>    <dbl>   <dbl>
-1 rnorm     0    10   1.96    NA    NA            1 mean        quantile 0.975 -0.573 
-2 rnorm     0   100   1.96    NA    NA            1 mean        quantile 0.975 -0.0818
-3 rexp     NA    10   3.69     1    NA            1 mean        quantile 0.975 -0.917 
-4 rexp     NA   100   3.69     1    NA            1 mean        quantile 0.975 -0.175 
-5 runif    NA    10   1.95    NA     2            1 mean        quantile 0.975 -0.174 
-6 runif    NA   100   1.95    NA     2            1 mean        quantile 0.975 -0.0183
-Number of data generating functions: 6
-Number of analyzing procedures: 1
-Number of replications: 1000
-Estimated replications per hour: 763125
-Start of the simulation: 2020-09-23 18:15:41
-End of the simulation: 2020-09-23 18:15:46
-

In the same fashion one could write a data analyzing function with a parameter .truth. To go even a step further, we store the analytic quantile function in the .truth column:

-
dg <- dplyr::bind_rows(
-  expand_tibble(fun = c("rnorm"), mean = 0, n = c(10L, 1000L),
-                .truth = list(function(prob) qnorm(prob, mean = 0))),
-  expand_tibble(fun = c("rexp"), rate = 1, n = c(10L, 1000L),
-                .truth = list(function(prob) qexp(prob, rate = 1))),
-  expand_tibble(fun = c("runif"), max = 2, n = c(10L, 1000L),
-                .truth = list(function(prob) qunif(prob, max = 2)))
-)
-bias_quantile <- function(x, prob, .truth) {
-  est <- quantile(x, probs = prob)
-  ret <- est - .truth[[1]](prob)
-  names(ret) <- "bias"
-  ret
-}
-pg <- expand_tibble(proc = "bias_quantile", prob = c(0.9, 0.975))
-eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
-                   ncpus = 1,
-                   summary_fun = list(mean = mean))
-
# A tibble: 12 x 11
-   fun    mean     n .truth  rate   max replications summary_fun proc           prob      bias
-   <chr> <dbl> <int> <list> <dbl> <dbl>        <int> <chr>       <chr>         <dbl>     <dbl>
- 1 rnorm     0    10 <fn>      NA    NA            1 mean        bias_quantile 0.9   -0.218   
- 2 rnorm     0    10 <fn>      NA    NA            1 mean        bias_quantile 0.975 -0.530   
- 3 rnorm     0  1000 <fn>      NA    NA            1 mean        bias_quantile 0.9   -0.00626 
- 4 rnorm     0  1000 <fn>      NA    NA            1 mean        bias_quantile 0.975 -0.0106  
- 5 rexp     NA    10 <fn>       1    NA            1 mean        bias_quantile 0.9   -0.275   
- 6 rexp     NA    10 <fn>       1    NA            1 mean        bias_quantile 0.975 -0.983   
- 7 rexp     NA  1000 <fn>       1    NA            1 mean        bias_quantile 0.9   -0.00226 
- 8 rexp     NA  1000 <fn>       1    NA            1 mean        bias_quantile 0.975 -0.0217  
- 9 runif    NA    10 <fn>      NA     2            1 mean        bias_quantile 0.9   -0.150   
-10 runif    NA    10 <fn>      NA     2            1 mean        bias_quantile 0.975 -0.173   
-11 runif    NA  1000 <fn>      NA     2            1 mean        bias_quantile 0.9   -0.000524
-12 runif    NA  1000 <fn>      NA     2            1 mean        bias_quantile 0.975 -0.00201 
-Number of data generating functions: 6
-Number of analyzing procedures: 2
-Number of replications: 1000
-Estimated replications per hour: 386468
-Start of the simulation: 2020-09-23 18:15:46
-End of the simulation: 2020-09-23 18:15:55
-

But one should keep in mind that if one calculates the quantile during the (post) analyzing phase that this is happens on replication level. To be more precise lets look at an excerpt of the pseudo code from the beginning of the vignette:

-
6.  for g in  {g_1, ..., g_k} 
-7.      for r in 1:replications (optionally in a parallel manner) 
-8.          data = g() 
-9.          for f in  {f_1, \ldots, f_L} 
-10.             append f(data) to the result object (optionally apply a post-analyze-function)
-

No matter if one extend the data analyzing function f_1, … f_L or the post-analyze-function with an argument .truth the calculation are made for every single replication during step 10. Hence, the operations are not vectorized!

-
-
-
-

-Some Examples

-

Note, the following code examples will use more computational resources. In order to prevent that these are checked/executed on the CRAN check farm, they are only evaluated if the environment variable NOT_CRAN is set to “true”

-
EVAL <- FALSE
-if (Sys.getenv("NOT_CRAN") == "true") {
-  EVAL <- TRUE
-}
-
-

-Sampling distribution of mean and median for normal and exponential distributed data

-

First we define how the data is generated, where the sample size should be 10 and 100:

-
dg <- dplyr::bind_rows(
-  expand_tibble(fun = c("rnorm"), mean = 1, n = c(10L, 100L)),
-  expand_tibble(fun = c("rexp"), rate = 1, n = c(10L, 100L))
-)
-dg
-

Afterwards we define how we want to analyze the data:

-
pg <- expand_tibble(proc = c("mean", "median"))
-pg
-

Finally, we conduct the simulation and visualize the results

-
et <- eval_tibbles(dg, pg, replications = 10^4, ncpus = 2)
-
-et$simulation %>%
-  ggplot(aes(x = results, color = interaction(fun, n), fill = interaction(fun, n))) +
-  geom_density(alpha = 0.3) +
-  facet_wrap(~ proc)
-
-
-

-Comparing bootstrap confidence intervals with classical studentized interval

-

We want to compare the confidence intervals that are generated by boot::boot.ci() and stats::t.test(). Unfortunately, boot::boot.ci() cannot be applied directly to the generated data sets. Therefore, we write a new function:

-
bootstrap_ci <- function(x, conf.level, R = 999) {
-  b <- boot::boot(x, function(d, i) {
-    n <- length(i)
-    c(
-      mean = mean(d[i]),
-      variance = (n - 1) * var(d[i]) / n^2
-    )
-  }, R = R)
-  boot::boot.ci(b, conf = conf.level, type = "all")
-}
-

Furthermore, boot::boot.ci() returns in general more than one confidence interval and the structures returned by boot::boot.ci() and stats::t.test() are also very different. One solution could be to write a function t_test() that calls stats::t.test(), modifies the returned object and additionally modify the function bootstrap_ci so that both function return objects with a unified structure. But instead of that we will implement a function that is later on passed to the argument post_analyze of eval_tibbles:

-
post_analyze <- function(o, .truth) {
-  if (class(o) == "htest") {
-  #post-process the object returned by t.test
-    ci <- o$conf.int
-    return(tibble::tibble(
-      type = "t.test",
-      aspect = c("covered", "ci_length"),
-      value = c(ci[1] <= .truth && .truth <= ci[2], ci[2] - ci[1])
-    ))
-  } else if (class(o) == "bootci") {
-  #post-process the object returned by boot.ci
-    method = c("normal", "basic", "student", "percent", "bca")
-    ret = o[method]
-    lower = unlist(purrr::map(ret, ~dplyr::nth(.x, -2)))
-    upper = sapply(ret, dplyr::last)
-    type = paste("boot", method, sep = "_")
-
-    return(
-      dplyr::bind_rows(
-      tibble::tibble(
-        type = type,
-        aspect = "covered",
-        value = as.integer(lower <= .truth & .truth <= upper)),
-      tibble::tibble(
-        type = type,
-        aspect = "ci_length",
-        value = upper - lower))
-    )
-  }
-}
-

As you can see, the objects returned are tibbles with more than one row. Summarizing the data over all replications will in general use the variable type and aspect as grouping variables. This can be achieved by using the parameter group_for_summary of eval_tibbles.

-
-

-Define and run the simulation

-

We want to generate normal-, uniform-, and exponential distributed data:

-
dg <- dplyr::bind_rows(
-  simTool::expand_tibble(fun = "rnorm", n = 10L, mean = 0, sd = sqrt(3), .truth = 0),
-  simTool::expand_tibble(fun = "runif", n = 10L, max = 6, .truth = 3),
-  simTool::expand_tibble(fun = "rexp", n = 10L, rate = 1 / sqrt(3), .truth = sqrt(3))
-)
-dg
-

and apply our functions that calculate the confidence intervals to it using two different confidence levels.

-
pg <- simTool::expand_tibble(
-  proc = c("t.test","bootstrap_ci"),
-  conf.level = c(0.9, 0.95)
-)
-pg
-

Note, that the structure of the objects returned are quite different which addressed by our function post_analyze. The variables type and aspect that are created by post_analyze are to distinguish the different confidence intervals. Since these variables are part of the result objects, eval_tibbles assumes that these variables are results. In order to summarize the results (calculating the mean) over all replications correctly we need to tell eval_tibbles that additional group variables are type and aspect:

-
et <- eval_tibbles(dg, pg,
-  replications = 10^3, ncpus = 2,
-  cluster_global_objects = "post_analyze",
-  post_analyze = post_analyze,
-  summary_fun = list(mean = mean),
-  group_for_summary = c("aspect", "type")
-)
-et
-

Finally, we can visualize the summarized results:

-
et$simulation %>%
-  ggplot(aes(x = fun, y = value, group = type, fill = type, label = round(value, 2))) +
-  geom_col(position = "dodge") +
-  geom_label(position = position_dodge(0.9), size = 3) +
-  theme(legend.position = "bottom") +
-  facet_grid(aspect ~ conf.level, scales = "free")
-
-
-

-A different implementation

-

Here we briefly realize the simulation differently by leveraging data analyzing functions with unified return-objects:

-
t_test = function(x, conf.level){
-  tt <- t.test(x, conf.level = conf.level)
-
-  # unify return
-  tibble::tibble(type = "t.test", lower = tt$conf.int[1], upper = tt$conf.int[2])
-}
-
-bootstrap_ci <- function(x, conf.level, R = 999) {
-  b <- boot::boot(x, function(d, i) {
-    n <- length(i)
-    c(
-      mean = mean(d[i]),
-      variance = (n - 1) * var(d[i]) / n^2
-    )
-  }, R = R)
-  ci <- boot::boot.ci(b, conf = conf.level, type = "all")
-  method = c("normal", "basic", "student", "percent", "bca")
-  ret = ci[method]
-  lower = unlist(purrr::map(ret, ~dplyr::nth(.x, -2)))
-  upper = sapply(ret, dplyr::last)
-  type = paste("boot", method, sep = "_")
-
-  # unify return
-  tibble::tibble(type = type, lower = lower, upper = upper)
-}
-
-dg <- dplyr::bind_rows(
-  simTool::expand_tibble(fun = "rnorm", n = 10L, mean = 0, sd = sqrt(3), .truth = 0),
-  simTool::expand_tibble(fun = "runif", n = 10L, max = 6, .truth = 3),
-  simTool::expand_tibble(fun = "rexp", n = 10L, rate = 1 / sqrt(3), .truth = sqrt(3))
-)
-
-pg <- simTool::expand_tibble(
-  proc = c("t_test","bootstrap_ci"),
-  conf.level = c(0.9, 0.95)
-) %>%
-  mutate(R = ifelse(proc == "bootstrap_ci", 100, NA))
-
-et <- eval_tibbles(dg, pg,
-  replications = 10^2, ncpus = 2
-)
-et
-
grps <- et$simulation %>%
-  select(-replications) %>%
-  select(fun:type) %>%
-  names
-
-et$simulation %>%
-  mutate(covered = lower <= .truth & .truth <= upper,
-         ci_length = upper - lower) %>%
-  group_by(.dots = grps) %>%
-  summarise(coverage = mean(covered),
-            ci_length = mean(ci_length))
-
-
-
-
- - - -
- - - -
-

Developed by Marsel Scheer.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 12ba60b..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,150 +0,0 @@ - - - - - - - - -Authors • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
- - - - -
- -
-
-
-

Authors

-
- -
    -
  • -

    Marsel Scheer. Author, maintainer. -

    -
    • -
- -
- -
- - - -
-
-

Developed by Marsel Scheer.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - - - diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css deleted file mode 100644 index 5a85941..0000000 --- a/docs/bootstrap-toc.css +++ /dev/null @@ -1,60 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ - -/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ - -/* All levels of nav */ -nav[data-toggle='toc'] .nav > li > a { - display: block; - padding: 4px 20px; - font-size: 13px; - font-weight: 500; - color: #767676; -} -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 19px; - color: #563d7c; - text-decoration: none; - background-color: transparent; - border-left: 1px solid #563d7c; -} -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 18px; - font-weight: bold; - color: #563d7c; - background-color: transparent; - border-left: 2px solid #563d7c; -} - -/* Nav: second level (shown on .active) */ -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} -nav[data-toggle='toc'] .nav .nav > li > a { - padding-top: 1px; - padding-bottom: 1px; - padding-left: 30px; - font-size: 12px; - font-weight: normal; -} -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 29px; -} -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 28px; - font-weight: 500; -} - -/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ -nav[data-toggle='toc'] .nav > .active > ul { - display: block; -} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js deleted file mode 100644 index 1cdd573..0000000 --- a/docs/bootstrap-toc.js +++ /dev/null @@ -1,159 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $('
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  • '); - $li.append($a); - return $li; - }, - - generateNavItem: function(headingEl) { - var anchor = this.generateAnchor(headingEl); - var $heading = $(headingEl); - var text = $heading.data('toc-text') || $heading.text(); - return this.generateNavEl(anchor, text); - }, - - // Find the first heading level (`

    `, then `

    `, etc.) that has more than one element. Defaults to 1 (for `

    `). - getTopLevel: function($scope) { - for (var i = 1; i <= 6; i++) { - var $headings = this.findOrFilter($scope, 'h' + i); - if ($headings.length > 1) { - return i; - } - } - - return 1; - }, - - // returns the elements for the top level, and the next below it - getHeadings: function($scope, topLevel) { - var topSelector = 'h' + topLevel; - - var secondaryLevel = topLevel + 1; - var secondarySelector = 'h' + secondaryLevel; - - return this.findOrFilter($scope, topSelector + ',' + secondarySelector); - }, - - getNavLevel: function(el) { - return parseInt(el.tagName.charAt(1), 10); - }, - - populateNav: function($topContext, topLevel, $headings) { - var $context = $topContext; - var $prevNav; - - var helpers = this; - $headings.each(function(i, el) { - var $newNav = helpers.generateNavItem(el); - var navLevel = helpers.getNavLevel(el); - - // determine the proper $context - if (navLevel === topLevel) { - // use top level - $context = $topContext; - } else if ($prevNav && $context === $topContext) { - // create a new level of the tree and switch to it - $context = helpers.createChildNavList($prevNav); - } // else use the current $context - - $context.append($newNav); - - $prevNav = $newNav; - }); - }, - - parseOps: function(arg) { - var opts; - if (arg.jquery) { - opts = { - $nav: arg - }; - } else { - opts = arg; - } - opts.$scope = opts.$scope || $(document.body); - return opts; - } - }, - - // accepts a jQuery object, or an options object - init: function(opts) { - opts = this.helpers.parseOps(opts); - - // ensure that the data attribute is in place for styling - opts.$nav.attr('data-toggle', 'toc'); - - var $topContext = this.helpers.createChildNavList(opts.$nav); - var topLevel = this.helpers.getTopLevel(opts.$scope); - var $headings = this.helpers.getHeadings(opts.$scope, topLevel); - this.helpers.populateNav($topContext, topLevel, $headings); - } - }; - - $(function() { - $('nav[data-toggle="toc"]').each(function(i, el) { - var $nav = $(el); - Toc.init($nav); - }); - }); -})(); diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; - margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504c..0000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); - $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index c48cbaf..0000000 --- a/docs/index.html +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - -Conduct Simulation Studies with a Minimal Amount of Source Code • simTool - - - - - - - - - - -
    -
    - - - - -
    -
    - - -
    -

    -simTool

    -

    An R-Package that facilitates simulation studies. It disengages the researcher from administrative source code.

    -

    The simTool package is designed for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the simTool package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with the simTool package. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.

    -
    -

    -Example

    -

    This small simulation (using 4 cores) illustrates how the confidence interval based on the t-distribution performs on exponential distributed random variables. The following lines generate exponential distributed random variables of size 10, 50, 100, and 1000. Afterwards the t.test using confidence levels 0.8, 0.9, 0.95 are applied. This is repeated 1000 times to estimate the coverage:

    -
    library(simTool)
-dg <- expand_tibble(fun = "rexp", rate = 10, n = c(10L, 50L, 100L, 1000L))
-pg <- expand_tibble(proc = "t.test", conf.level = c(0.8, 0.9, 0.95))
-et <- eval_tibbles(dg, pg,
-  ncpus = 4,
-  replications = 10^3,
-  post_analyze = function(ttest) tibble::tibble(
-    coverage = ttest$conf.int[1] <= 1 / 10 && 1 / 10 <= ttest$conf.int[2]),
-  summary_fun = list(mean = mean)
-)
-#>   |                                                                              |                                                                      |   0%  |                                                                              |==================                                                    |  25%  |                                                                              |===================================                                   |  50%  |                                                                              |====================================================                  |  75%  |                                                                              |======================================================================| 100%
-et
-#> # A tibble: 12 x 8
-#>    fun    rate     n replications summary_fun proc   conf.level coverage
-#>    <chr> <dbl> <int>        <int> <chr>       <chr>       <dbl>    <dbl>
-#>  1 rexp     10    10            1 mean        t.test       0.8     0.754
-#>  2 rexp     10    10            1 mean        t.test       0.9     0.855
-#>  3 rexp     10    10            1 mean        t.test       0.95    0.905
-#>  4 rexp     10    50            1 mean        t.test       0.8     0.808
-#>  5 rexp     10    50            1 mean        t.test       0.9     0.905
-#>  6 rexp     10    50            1 mean        t.test       0.95    0.945
-#>  7 rexp     10   100            1 mean        t.test       0.8     0.792
-#>  8 rexp     10   100            1 mean        t.test       0.9     0.895
-#>  9 rexp     10   100            1 mean        t.test       0.95    0.936
-#> 10 rexp     10  1000            1 mean        t.test       0.8     0.796
-#> 11 rexp     10  1000            1 mean        t.test       0.9     0.897
-#> 12 rexp     10  1000            1 mean        t.test       0.95    0.953
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 3
-#> Number of replications: 1000
-#> Estimated replications per hour: 614008
-#> Start of the simulation: 2020-09-16 19:53:05
-#> End of the simulation: 2020-09-16 19:53:11
    -
    -
    -

    -Installation

    -

    You can install simTool from github with:

    -
    install.packages("simTool")
-# install.packages("devtools")
-devtools::install_github("MarselScheer/simTool")
    -

    Or from CRAN with:

    -
    install.packages("simTool")
    -
    -
    -

    -Test suite

    -

    With tinytest you can rerun the test for simTool by calling

    -
    # install.packages("tinytest")
-tinytest::test_package("simTool")
    -
    -
    - -
    - - -
    - - -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 86658e8..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - - - -Changelog • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Changelog

    - Source: NEWS.md -
    - -
    -

    -Version 1.1.7 2020-09-22 -

    -
      -
    • During a replication data is generated once and then used by all data analyzing functions. This was already the case under the default setting where the generated data sets are stored by eval_tibbles() and is now also the case were the generated data is discarded by the eval_tibbles() function.
      • -
    -
    -
    -

    -Version 1.1.6 2020-05-17 -

    -

    Misc:

    -
      -
    • internal updated for dplyr 1.0.0 and vctrs 0.3.0
      • -
    • internal correction based on lintr-findings
      • -
    -
    -
    -

    -Version 1.1.5 2020-03-15 -

    -

    Misc:

    -
      -
    • removing functions expandGrid() and evalGrid() (which were marked as deprecated in version 1.1.0) and the corresponding dependencies with reshape and plyr
      • -
    • retrieve the RNG kind after parallel computation
      • -
    -
    -
    -

    -Version 1.1.4 2019-09-14 -

    -

    Misc:

    -
      -
    • adaptions to address the new release of tidyr 1.0.0
      • -
    -

    Note:

    -
      -
    • due to https://github.com/r-lib/vctrs/issues/530 reshaping the simulation results can be extremely slow. That costly reshaping will not happen if the parameter simplify is set to FALSE in simTool::eval_tibbles.
      • -
    -
    -
    -

    -Version 1.1.3 2019-03-22 -

    -

    Misc:

    -
      -
    • test cases update to address the new release of dplyr 0.8.0
      • -
    -
    -
    -

    -Version 1.1.2 2019-02-02 -

    -

    Misc:

    - -
    -
    -

    -Version 1.1.1 2019-01-13 -

    -

    Misc:

    -
      -
    • .truth-functionality added, i.e. the parameters of the data generation (or alternatively a column of the data generating matrix with the name .truth) is passed to the the data analyzing functions, see the vignette for details
      • -
    • Unnesting of the simulation results improved
      • -
    -
    -
    -

    -Version 1.1.0 2018-03-26 -

    -

    Misc:

    -
      -
    • Refactoring in order to remove the dependency on reshape and plyr
      • -
    • The simulation itself is now a tibble instead of a list of lists
      • -
    -
    -
    -

    -Version 1.0.3 2014-10-25 -

    -

    Misc:

    -
      -
    • Adapted how libraries are loaded onto the cluster
      • -
    -
    -
    -

    -Version 1.0.2 2014-10-15 -

    -

    New Features:

    -
      -
    • The convenient function meanAndNormCI added
      • -
    -

    Misc:

    -
      -
    • Two parameter renamed (post.proc to summary.fun and value.fun to convert.result.fun). Of course, renaming parameters is one of the worst things one can do. On the other hand, only very few users will be affected by these changes.
      • -
    -
    -
    -

    -Version 1.0.1 Unreleased -

    -

    New Features:

    -
      -
    • summarizing functions process also logical results (not only numeric)
      • -
    -

    Misc:

    -
      -
    • HTML vignette (LaTeX not necessary anymore)
      • -
    • additional documentation (static pdf in JSS style)
      • -
    -
    -
    -

    -Version 1.0 2014-07-08 -

    -

    Initial release

    -
      -
    • parallel computing via parallel and the ideas of L’Ecuyer 1999, 2002 for random numbers
      • -
    • fallback capability
      • -
    • text progress bar
      • -
    • estimation of the number of replications per hour
      • -
    -
    -
    - - - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index c01e592..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,367 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; 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- } -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 7e7048f..0000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,108 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index 4548ee5..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,10 +0,0 @@ -pandoc: 2.7.3 -pkgdown: 1.5.1 -pkgdown_sha: ~ -articles: - simTool: simTool.html -last_built: 2020-09-23T16:15Z -urls: - reference: http://MarselScheer.github.io/simTool/reference - article: http://MarselScheer.github.io/simTool/articles - diff --git a/docs/reference/eval_tibbles.html b/docs/reference/eval_tibbles.html deleted file mode 100644 index 5c9450c..0000000 --- a/docs/reference/eval_tibbles.html +++ /dev/null @@ -1,549 +0,0 @@ - - - - - - - - -Workhorse for simulation studies — eval_tibbles • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Workhorse for simulation studies

    - Source: R/eval_tibbles.R - -
    - -
    -

    Generates data according to all provided - constellations in data_tibble and applies - all provided constellations in proc_tibble - to them.

    -
    - - 
    eval_tibbles(
-  data_grid,
-  proc_grid = expand_tibble(proc = "length"),
-  replications = 1,
-  discard_generated_data = FALSE,
-  post_analyze = identity,
-  summary_fun = NULL,
-  group_for_summary = NULL,
-  ncpus = 1L,
-  cluster = NULL,
-  cluster_seed = rep(12345, 6),
-  cluster_libraries = NULL,
-  cluster_global_objects = NULL,
-  envir = globalenv(),
-  simplify = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data_grid

    a data.frame or tibble where the first column -is a character vector with function names. The other -columns contain parameters for the functions specified -in the first column. Parameters with NA are ignored. If a column with name -.truth exist, then the corresponding entry is passed to functions - generated from -proc_grid and the function specified in post_analyze.

    proc_grid

    similar as data_grid the first -column must contain function names. The other columns -contain parameters for the functions specified in the -first column. The data generated according to -data_grid will always be passed to the first -unspecified argument of the functions specified in the first -column of proc_grid. If a function specified in -proc_grid has an argument .truth, then the corresponding -entry in the -.truth column from data_grid is passed to the -.truth parameter or if no column .truth exist in -data_grid, then all parameters used -for the data generation are passed to the .truth parameter.

    replications

    number of replications for the simulation

    discard_generated_data

    if TRUE the generated -data is deleted after all function constellations in -proc_grid have been applied. Otherwise, ALL -generated data sets will be part of the returned object.

    post_analyze

    this is a convenience function, that is applied -directly after the data analyzing function. If this function has an -argument .truth, then the corresponding entry in the -.truth column from data_grid is passed to the .truth -parameter or if no column .truth exist in data_grid, -then all parameters used for the data generation are passed to the -.truth parameter.

    summary_fun

    named list of univariate function to summarize the -results (numeric or logical) over the replications, e.g. -list(mean = mean, sd = sd).

    group_for_summary

    if the result returned by the data analyzing -function or post_analyze -is a data.frame with more than one row, one usually is interested -in summarizing the results while grouping for some variables. This group -variables can be passed as a character vector into group_for_summary

    ncpus

    a cluster of ncpus workers (R-processes) -is created on the local machine to conduct the -simulation. If ncpus -equals one no cluster is created and the simulation -is conducted by the current R-process.

    cluster

    a cluster generated by the parallel -package that will be used to conduct the simulation. -If cluster is specified, then ncpus will -be ignored.

    cluster_seed

    if the simulation is done in parallel -manner, then the combined multiple-recursive generator from L'Ecuyer (1999) -is used to generate random numbers. Thus cluster_seed must be a -(signed) integer vector of length 6. -The 6 elements of the seed are internally regarded as -32-bit unsigned integers. Neither the first three nor the last three -should be all zero, and they are limited to less than 4294967087 and -4294944443 respectively.

    cluster_libraries

    a character vector specifying -the packages that should be loaded by the workers.

    cluster_global_objects

    a character vector specifying -the names of R objects in the global environment that should -be exported to the global environment of every worker.

    envir

    must be provided if the functions specified -in data_grid or proc_grid are not part -of the global environment.

    simplify

    usually the result column is nested, by default it is -tried to unnest it.

    - -

    Value

    - -

    The returned object list of the class - eval_tibbles, where the element simulations contain - the results of the simulation.

    -

    Note

    - -

    If cluster is provided by the user the - function eval_tibbles will NOT stop the cluster. - This has to be done by the user. Conducting parallel - simulations by specifying ncpus will internally - create a cluster and stop it after the simulation - is done.

    - -

    Examples

    - 
    rng <- function(data, ...) {
-  ret <- range(data)
-  names(ret) <- c("min", "max")
-  ret
-}
-
-### The following line is only necessary
-### if the examples are not executed in the global
-### environment, which for instance is the case when
-### the oneline-documentation
-### http://marselscheer.github.io/simTool/reference/eval_tibbles.html
-### is build. In such case eval_tibble() would search the
-### above defined function rng() in the global environment where
-### it does not exist!
-eval_tibbles <- purrr::partial(eval_tibbles, envir = environment())
-
-dg <- expand_tibble(fun = "rnorm", n = c(5L, 10L))
-pg <- expand_tibble(proc = c("rng", "median", "length"))
-
-eval_tibbles(dg, pg, rep = 2, simplify = FALSE)
    #> # A tibble: 12 x 5
-#>    fun       n replications proc   results  
-#>    <chr> <int>        <int> <chr>  <list>   
-#>  1 rnorm     5            1 rng    <dbl [2]>
-#>  2 rnorm     5            1 median <dbl [1]>
-#>  3 rnorm     5            1 length <int [1]>
-#>  4 rnorm     5            2 rng    <dbl [2]>
-#>  5 rnorm     5            2 median <dbl [1]>
-#>  6 rnorm     5            2 length <int [1]>
-#>  7 rnorm    10            1 rng    <dbl [2]>
-#>  8 rnorm    10            1 median <dbl [1]>
-#>  9 rnorm    10            1 length <int [1]>
-#> 10 rnorm    10            2 rng    <dbl [2]>
-#> 11 rnorm    10            2 median <dbl [1]>
-#> 12 rnorm    10            2 length <int [1]>
-#> Number of data generating functions: 2
-#> Number of analyzing procedures: 3
-#> Number of replications: 2
-#> Estimated replications per hour: 4781347
-#> Start of the simulation: 2020-09-23 18:15:25
-#> End of the simulation: 2020-09-23 18:15:25
    eval_tibbles(dg, pg, rep = 2)
    #> # A tibble: 16 x 5
-#>    fun       n replications proc   results
-#>    <chr> <int>        <int> <chr>    <dbl>
-#>  1 rnorm     5            1 rng     0.112 
-#>  2 rnorm     5            1 rng     1.62  
-#>  3 rnorm     5            1 median  0.244 
-#>  4 rnorm     5            1 length  5     
-#>  5 rnorm     5            2 rng    -1.91  
-#>  6 rnorm     5            2 rng     1.07  
-#>  7 rnorm     5            2 median -0.279 
-#>  8 rnorm     5            2 length  5     
-#>  9 rnorm    10            1 rng    -1.91  
-#> 10 rnorm    10            1 rng     2.76  
-#> 11 rnorm    10            1 median  0.0583
-#> 12 rnorm    10            1 length 10     
-#> 13 rnorm    10            2 rng    -2.27  
-#> 14 rnorm    10            2 rng     2.68  
-#> 15 rnorm    10            2 median  0.0244
-#> 16 rnorm    10            2 length 10     
-#> Number of data generating functions: 2
-#> Number of analyzing procedures: 3
-#> Number of replications: 2
-#> Estimated replications per hour: 14624207
-#> Start of the simulation: 2020-09-23 18:15:25
-#> End of the simulation: 2020-09-23 18:15:25
    eval_tibbles(dg, pg,
-  rep = 2,
-  post_analyze = purrr::compose(as.data.frame, t)
-)
    #> # A tibble: 12 x 7
-#>    fun       n replications proc     min   max     V1
-#>    <chr> <int>        <int> <chr>  <dbl> <dbl>  <dbl>
-#>  1 rnorm     5            1 rng    -1.18  1.11 NA    
-#>  2 rnorm     5            1 median NA    NA    -0.246
-#>  3 rnorm     5            1 length NA    NA     5    
-#>  4 rnorm     5            2 rng    -1.70  1.07 NA    
-#>  5 rnorm     5            2 median NA    NA     0.132
-#>  6 rnorm     5            2 length NA    NA     5    
-#>  7 rnorm    10            1 rng    -1.47  1.34 NA    
-#>  8 rnorm    10            1 median NA    NA     0.260
-#>  9 rnorm    10            1 length NA    NA    10    
-#> 10 rnorm    10            2 rng    -2.61  1.92 NA    
-#> 11 rnorm    10            2 median NA    NA     0.495
-#> 12 rnorm    10            2 length NA    NA    10    
-#> Number of data generating functions: 2
-#> Number of analyzing procedures: 3
-#> Number of replications: 2
-#> Estimated replications per hour: 830760
-#> Start of the simulation: 2020-09-23 18:15:25
-#> End of the simulation: 2020-09-23 18:15:25
    eval_tibbles(dg, pg, rep = 2, summary_fun = list(mean = mean, sd = sd))
    #> # A tibble: 12 x 8
-#>    fun       n replications summary_fun proc       min    max  value
-#>    <chr> <int>        <int> <chr>       <chr>    <dbl>  <dbl>  <dbl>
-#>  1 rnorm     5            1 mean        rng    -0.196   1.37  NA    
-#>  2 rnorm     5            1 mean        median NA      NA      0.716
-#>  3 rnorm     5            1 mean        length NA      NA      5    
-#>  4 rnorm     5            1 sd          rng     0.224   0.431 NA    
-#>  5 rnorm     5            1 sd          median NA      NA      0.325
-#>  6 rnorm     5            1 sd          length NA      NA      0    
-#>  7 rnorm    10            1 mean        rng    -1.72    1.55  NA    
-#>  8 rnorm    10            1 mean        median NA      NA     -0.185
-#>  9 rnorm    10            1 mean        length NA      NA     10    
-#> 10 rnorm    10            1 sd          rng     0.0509  0.812 NA    
-#> 11 rnorm    10            1 sd          median NA      NA      0.621
-#> 12 rnorm    10            1 sd          length NA      NA      0    
-#> Number of data generating functions: 2
-#> Number of analyzing procedures: 3
-#> Number of replications: 2
-#> Estimated replications per hour: 87096
-#> Start of the simulation: 2020-09-23 18:15:25
-#> End of the simulation: 2020-09-23 18:15:25
    
-regData <- function(n, SD) {
-  data.frame(
-    x = seq(0, 1, length = n),
-    y = rnorm(n, sd = SD)
-  )
-}
-
-eg <- eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  replications = 3
-)
-eg
    #> # A tibble: 12 x 7
-#>    fun         n    SD replications proc  formula  results
-#>    <chr>   <int> <int>        <int> <chr> <chr>    <list> 
-#>  1 regData     5     1            1 lm    y~x      <lm>   
-#>  2 regData     5     1            1 lm    y~I(x^2) <lm>   
-#>  3 regData     5     1            2 lm    y~x      <lm>   
-#>  4 regData     5     1            2 lm    y~I(x^2) <lm>   
-#>  5 regData     5     1            3 lm    y~x      <lm>   
-#>  6 regData     5     1            3 lm    y~I(x^2) <lm>   
-#>  7 regData     5     2            1 lm    y~x      <lm>   
-#>  8 regData     5     2            1 lm    y~I(x^2) <lm>   
-#>  9 regData     5     2            2 lm    y~x      <lm>   
-#> 10 regData     5     2            2 lm    y~I(x^2) <lm>   
-#> 11 regData     5     2            3 lm    y~x      <lm>   
-#> 12 regData     5     2            3 lm    y~I(x^2) <lm>   
-#> Number of data generating functions: 2
-#> Number of analyzing procedures: 2
-#> Number of replications: 3
-#> Estimated replications per hour: 505056
-#> Start of the simulation: 2020-09-23 18:15:25
-#> End of the simulation: 2020-09-23 18:15:26
    
-presever_rownames <- function(mat) {
-  rn <- rownames(mat)
-  ret <- tibble::as_tibble(mat)
-  ret$term <- rn
-  ret
-}
-
-eg <- eval_tibbles(
-  expand_tibble(fun = "regData", n = 5L, SD = 1:2),
-  expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
-  post_analyze = purrr::compose(presever_rownames, coef, summary),
-  # post_analyze = broom::tidy, # is a nice out of the box alternative
-  summary_fun = list(mean = mean, sd = sd),
-  group_for_summary = "term",
-  replications = 3
-)
-eg$simulation
    #> # A tibble: 16 x 12
-#>    fun       n    SD replications summary_fun proc  formula term  Estimate
-#>    <chr> <int> <int>        <int> <chr>       <chr> <chr>   <chr>    <dbl>
-#>  1 regD…     5     1            1 mean        lm    y~x     (Int…   -0.137
-#>  2 regD…     5     1            1 mean        lm    y~x     x        0.148
-#>  3 regD…     5     1            1 mean        lm    y~I(x^… (Int…   -0.121
-#>  4 regD…     5     1            1 mean        lm    y~I(x^… I(x^…    0.154
-#>  5 regD…     5     1            1 sd          lm    y~x     (Int…    0.330
-#>  6 regD…     5     1            1 sd          lm    y~x     x        0.298
-#>  7 regD…     5     1            1 sd          lm    y~I(x^… (Int…    0.240
-#>  8 regD…     5     1            1 sd          lm    y~I(x^… I(x^…    0.913
-#>  9 regD…     5     2            1 mean        lm    y~x     (Int…   -1.05 
-#> 10 regD…     5     2            1 mean        lm    y~x     x        2.58 
-#> 11 regD…     5     2            1 mean        lm    y~I(x^… (Int…   -0.851
-#> 12 regD…     5     2            1 mean        lm    y~I(x^… I(x^…    2.91 
-#> 13 regD…     5     2            1 sd          lm    y~x     (Int…    0.754
-#> 14 regD…     5     2            1 sd          lm    y~x     x        0.492
-#> 15 regD…     5     2            1 sd          lm    y~I(x^… (Int…    0.667
-#> 16 regD…     5     2            1 sd          lm    y~I(x^… I(x^…    0.655
-#> # … with 3 more variables: `Std. Error` <dbl>, `t value` <dbl>,
-#> #   `Pr(>|t|)` <dbl>
    
-dg <- expand_tibble(fun = "rexp", rate = c(10, 100), n = c(50L, 100L))
-pg <- expand_tibble(proc = c("t.test"), conf.level = c(0.8, 0.9, 0.95))
-et <- eval_tibbles(dg, pg,
-  ncpus = 1,
-  replications = 10^1,
-  post_analyze = function(ttest, .truth) {
-    mu <- 1 / .truth$rate
-    ttest$conf.int[1] <= mu && mu <= ttest$conf.int[2]
-  },
-  summary_fun = list(mean = mean, sd = sd)
-)
-et
    #> # A tibble: 24 x 8
-#>    fun    rate     n replications summary_fun proc   conf.level value
-#>    <chr> <dbl> <int>        <int> <chr>       <chr>       <dbl> <dbl>
-#>  1 rexp     10    50            1 mean        t.test       0.8  0.9  
-#>  2 rexp     10    50            1 mean        t.test       0.9  0.9  
-#>  3 rexp     10    50            1 mean        t.test       0.95 0.9  
-#>  4 rexp     10    50            1 sd          t.test       0.8  0.316
-#>  5 rexp     10    50            1 sd          t.test       0.9  0.316
-#>  6 rexp     10    50            1 sd          t.test       0.95 0.316
-#>  7 rexp    100    50            1 mean        t.test       0.8  0.6  
-#>  8 rexp    100    50            1 mean        t.test       0.9  0.7  
-#>  9 rexp    100    50            1 mean        t.test       0.95 0.7  
-#> 10 rexp    100    50            1 sd          t.test       0.8  0.516
-#> # … with 14 more rows
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 3
-#> Number of replications: 10
-#> Estimated replications per hour: 247445
-#> Start of the simulation: 2020-09-23 18:15:26
-#> End of the simulation: 2020-09-23 18:15:26
    
-dg <- dplyr::bind_rows(
-  expand_tibble(fun = "rexp", rate = 10, .truth = 1 / 10, n = c(50L, 100L)),
-  expand_tibble(fun = "rnorm", .truth = 0, n = c(50L, 100L))
-)
-pg <- expand_tibble(proc = c("t.test"), conf.level = c(0.8, 0.9, 0.95))
-et <- eval_tibbles(dg, pg,
-  ncpus = 1,
-  replications = 10^1,
-  post_analyze = function(ttest, .truth) {
-    ttest$conf.int[1] <= .truth && .truth <= ttest$conf.int[2]
-  },
-  summary_fun = list(mean = mean, sd = sd)
-)
-et
    #> # A tibble: 24 x 9
-#>    fun    rate .truth     n replications summary_fun proc   conf.level value
-#>    <chr> <dbl>  <dbl> <int>        <int> <chr>       <chr>       <dbl> <dbl>
-#>  1 rexp     10    0.1    50            1 mean        t.test       0.8  0.9  
-#>  2 rexp     10    0.1    50            1 mean        t.test       0.9  0.9  
-#>  3 rexp     10    0.1    50            1 mean        t.test       0.95 1    
-#>  4 rexp     10    0.1    50            1 sd          t.test       0.8  0.316
-#>  5 rexp     10    0.1    50            1 sd          t.test       0.9  0.316
-#>  6 rexp     10    0.1    50            1 sd          t.test       0.95 0    
-#>  7 rexp     10    0.1   100            1 mean        t.test       0.8  0.6  
-#>  8 rexp     10    0.1   100            1 mean        t.test       0.9  0.7  
-#>  9 rexp     10    0.1   100            1 mean        t.test       0.95 0.8  
-#> 10 rexp     10    0.1   100            1 sd          t.test       0.8  0.516
-#> # … with 14 more rows
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 3
-#> Number of replications: 10
-#> Estimated replications per hour: 249072
-#> Start of the simulation: 2020-09-23 18:15:26
-#> End of the simulation: 2020-09-23 18:15:26
    ### need to remove the locally adapted eval_tibbles()
-### otherwise executing the examples would mask
-### eval_tibbles from simTool-namespace.
-rm(eval_tibbles)
    -
    - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/expand_tibble.html b/docs/reference/expand_tibble.html deleted file mode 100644 index 5274430..0000000 --- a/docs/reference/expand_tibble.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - - -Creates a <code>tibble</code> from All Combinations — expand_tibble • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Creates a tibble from All Combinations

    - Source: R/expand_tibble.R - -
    - -
    -

    Actually a wrapper for expand.grid, but - character vectors will stay as characters.

    -
    - - 
    expand_tibble(...)
    - -

    Arguments

    - - - - - - -
    ...

    vectors, factors or a list containing these.

    - -

    Value

    - -

    See expand.grid but instead of a - data.frame - a tibble is returned.

    -

    See also

    - -

    expand.grid

    - -

    Examples

    - 
    
-expand_tibble(fun = "rnorm", mean = 1:4, sd = 2:5)
    #> # A tibble: 16 x 3
-#>    fun    mean    sd
-#>    <chr> <int> <int>
-#>  1 rnorm     1     2
-#>  2 rnorm     2     2
-#>  3 rnorm     3     2
-#>  4 rnorm     4     2
-#>  5 rnorm     1     3
-#>  6 rnorm     2     3
-#>  7 rnorm     3     3
-#>  8 rnorm     4     3
-#>  9 rnorm     1     4
-#> 10 rnorm     2     4
-#> 11 rnorm     3     4
-#> 12 rnorm     4     4
-#> 13 rnorm     1     5
-#> 14 rnorm     2     5
-#> 15 rnorm     3     5
-#> 16 rnorm     4     5
    -
    - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 3ff5d81..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,195 +0,0 @@ - - - - - - - - -Function reference • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Reference

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    Define functions for generating and analyzing data

    -

    -
    -

    expand_tibble()

    -

    Creates a tibble from All Combinations

    -

    Perfom the simulation (on more than one CPU or a cluster)

    -

    -
    -

    eval_tibbles()

    -

    Workhorse for simulation studies

    -
    - - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/print.eval_tibbles.html b/docs/reference/print.eval_tibbles.html deleted file mode 100644 index cad9192..0000000 --- a/docs/reference/print.eval_tibbles.html +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - - -Printing simulation results — print.eval_tibbles • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Printing simulation results

    - Source: R/print.eval_tibbles.R - -
    - -
    -

    Prints objects created by eval_tibbles()

    -
    - - 
    # S3 method for eval_tibbles
-print(x, ...)
    - -

    Arguments

    - - - - - - - - - - -
    x

    object of class eval_tibbles

    ...

    not used. only necessary to define the function consistently -with respect to print(x, ...)

    - - -
    - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/simTool-package.html b/docs/reference/simTool-package.html deleted file mode 100644 index 49ee17c..0000000 --- a/docs/reference/simTool-package.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Conduct Simulation Studies with a Minimal Amount of Source Code — simTool-package • simTool - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conduct Simulation Studies with a Minimal Amount of Source Code

    - Source: R/simTool-package.R - -
    - -
    -

    The simTool package is designed for statistical simulations that -have two components. One component generates the data and the other one -analyzes the data. The main aims of the simTool package are the -reduction -of the administrative source code (mainly loops and management code for the -results) and a simple applicability of the package that allows the user to -quickly learn how to work with the simTool package. Parallel -computing is -also supported. Finally, convenient functions are provided to summarize the -simulation results.

    -
    - - - -

    Details

    - - - - - - - - -
    Package:simTool
    Type:Package
    Version:1.0.3
    Date:2013-02-21
    License:GPL-3
    -

    evalGrids is the workhorse. as.data.frame is function - coercing the result object of evalGrids to - a data.frame. expandGrid is only a convenient function

    - -

    Examples

    - 
    
-dg <- expand_tibble(fun = "rexp", n = c(10, 20), rate = 1:2)
-pg <- expand_tibble(proc = "summary")
-eval_tibbles(dg, pg, replications = 3)
    #> # A tibble: 12 x 6
-#>    fun       n  rate replications proc    results    
-#>    <chr> <dbl> <int>        <int> <chr>   <list>     
-#>  1 rexp     10     1            1 summary <table [6]>
-#>  2 rexp     10     1            2 summary <table [6]>
-#>  3 rexp     10     1            3 summary <table [6]>
-#>  4 rexp     20     1            1 summary <table [6]>
-#>  5 rexp     20     1            2 summary <table [6]>
-#>  6 rexp     20     1            3 summary <table [6]>
-#>  7 rexp     10     2            1 summary <table [6]>
-#>  8 rexp     10     2            2 summary <table [6]>
-#>  9 rexp     10     2            3 summary <table [6]>
-#> 10 rexp     20     2            1 summary <table [6]>
-#> 11 rexp     20     2            2 summary <table [6]>
-#> 12 rexp     20     2            3 summary <table [6]>
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 1
-#> Number of replications: 3
-#> Estimated replications per hour: 2080202
-#> Start of the simulation: 2020-05-16 19:03:17
-#> End of the simulation: 2020-05-16 19:03:17
    eval_tibbles(dg, pg, replications = 3, summary_fun = list(mean = mean))
    #> # A tibble: 4 x 12
-#>   fun       n  rate replications summary_fun proc    Min. `1st Qu.` Median  Mean
-#>   <chr> <dbl> <int>        <int> <chr>       <chr>  <dbl>     <dbl>  <dbl> <dbl>
-#> 1 rexp     10     1            1 mean        summ… 0.0735     0.307  0.593 0.757
-#> 2 rexp     20     1            1 mean        summ… 0.0255     0.458  0.724 0.906
-#> 3 rexp     10     2            1 mean        summ… 0.0240     0.125  0.243 0.538
-#> 4 rexp     20     2            1 mean        summ… 0.0115     0.184  0.423 0.520
-#> # … with 2 more variables: `3rd Qu.` <dbl>, Max. <dbl>
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 1
-#> Number of replications: 3
-#> Estimated replications per hour: 622796
-#> Start of the simulation: 2020-05-16 19:03:17
-#> End of the simulation: 2020-05-16 19:03:17
    eval_tibbles(dg, pg, replications = 3,
-  summary_fun = list(mean = mean, sd = sd))
    #> # A tibble: 8 x 12
-#>   fun       n  rate replications summary_fun proc    Min. `1st Qu.` Median  Mean
-#>   <chr> <dbl> <int>        <int> <chr>       <chr>  <dbl>     <dbl>  <dbl> <dbl>
-#> 1 rexp     10     1            1 mean        summ… 0.0612    0.331   0.697 0.934
-#> 2 rexp     10     1            1 sd          summ… 0.0517    0.0561  0.224 0.255
-#> 3 rexp     20     1            1 mean        summ… 0.0439    0.282   0.694 0.876
-#> 4 rexp     20     1            1 sd          summ… 0.0388    0.0239  0.264 0.278
-#> 5 rexp     10     2            1 mean        summ… 0.0289    0.0998  0.430 0.529
-#> 6 rexp     10     2            1 sd          summ… 0.0206    0.0279  0.406 0.289
-#> 7 rexp     20     2            1 mean        summ… 0.0132    0.187   0.392 0.492
-#> 8 rexp     20     2            1 sd          summ… 0.0101    0.0648  0.161 0.176
-#> # … with 2 more variables: `3rd Qu.` <dbl>, Max. <dbl>
-#> Number of data generating functions: 4
-#> Number of analyzing procedures: 1
-#> Number of replications: 3
-#> Estimated replications per hour: 430229
-#> Start of the simulation: 2020-05-16 19:03:17
-#> End of the simulation: 2020-05-16 19:03:17
    -
    - -
    - - -
    -
    -

    Developed by Marsel Scheer.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index e1af83e..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,18 +0,0 @@ - - - - http://MarselScheer.github.io/simTool/index.html - - - http://MarselScheer.github.io/simTool/reference/eval_tibbles.html - - - http://MarselScheer.github.io/simTool/reference/expand_tibble.html - - - http://MarselScheer.github.io/simTool/reference/print.eval_tibbles.html - - - http://MarselScheer.github.io/simTool/articles/simTool.html - - diff --git a/man/expand_tibble.Rd b/man/expand_tibble.Rd index 157cc0d..be6c7e9 100644 --- a/man/expand_tibble.Rd +++ b/man/expand_tibble.Rd @@ -12,7 +12,7 @@ expand_tibble(...) \value{ See \code{\link{expand.grid}} but instead of a \code{\link{data.frame}} - a \code{\link{tibble}} is returned. + a \code{\link[tibble:tibble]{tibble}} is returned. } \description{ Actually a wrapper for \code{\link{expand.grid}}, but diff --git a/man/print.eval_tibbles.Rd b/man/print.eval_tibbles.Rd index d16ef8b..d9461e2 100644 --- a/man/print.eval_tibbles.Rd +++ b/man/print.eval_tibbles.Rd @@ -18,3 +18,4 @@ Prints objects created by \code{eval_tibbles()} \author{ Marsel Scheer } +\keyword{internal} diff --git a/renv.lock b/renv.lock index 93e7e1b..b53ce07 100644 --- a/renv.lock +++ b/renv.lock @@ -1,6 +1,6 @@ { "R": { - "Version": "4.0.2", + "Version": "4.1.0", "Repositories": [ { "Name": "CRAN", @@ -11,24 +11,24 @@ "Packages": { "MASS": { "Package": "MASS", - "Version": "7.3-51.5", + "Version": "7.3-54", "Source": "Repository", "Repository": "CRAN", - "Hash": "9efe80472b21189ebab1b74169808c26" + "Hash": "0e59129db205112e3963904db67fd0dc" }, "Matrix": { "Package": "Matrix", - "Version": "1.2-18", + "Version": "1.3-4", "Source": "Repository", - "Repository": "CRAN", - "Hash": "08588806cba69f04797dab50627428ed" + "Repository": "RSPM", + "Hash": "4ed05e9c9726267e4a5872e09c04587c" }, "R6": { "Package": "R6", - "Version": "2.4.1", + "Version": "2.5.1", "Source": "Repository", "Repository": "CRAN", - "Hash": "292b54f8f4b94669b08f94e5acce6be2" + "Hash": "470851b6d5d0ac559e9d01bb352b4021" }, "RColorBrewer": { "Package": "RColorBrewer", @@ -37,13 +37,6 @@ "Repository": "CRAN", "Hash": "e031418365a7f7a766181ab5a41a5716" }, - "Rcpp": { - "Package": "Rcpp", - "Version": "1.0.4.6", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "e652f23d8b1807cc975c51410d05b72f" - }, "askpass": { "Package": "askpass", "Version": "1.1", @@ -51,26 +44,12 @@ "Repository": "CRAN", "Hash": "e8a22846fff485f0be3770c2da758713" }, - "assertthat": { - "Package": "assertthat", - "Version": "0.2.1", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "50c838a310445e954bc13f26f26a6ecf" - }, "backports": { "Package": "backports", - "Version": "1.1.7", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "e9c8188eb82b137301493492d767a1ac" - }, - "badgecreatr": { - "Package": "badgecreatr", - "Version": "0.2.0", + "Version": "1.2.1", "Source": "Repository", "Repository": "CRAN", - "Hash": "5db59479f84ad4a0650e74a8d8edac70" + "Hash": "644043219fc24e190c2f620c1a380a69" }, "base64enc": { "Package": "base64enc", @@ -81,80 +60,136 @@ }, "boot": { "Package": "boot", - "Version": "1.3-24", + "Version": "1.3-28", "Source": "Repository", "Repository": "CRAN", - "Hash": "72557d88b5f42f01221dfa436de99301" + "Hash": "0baa960e3b49c6176a4f42addcbacc59" + }, + "brio": { + "Package": "brio", + "Version": "1.1.2", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "2f01e16ff9571fe70381c7b9ae560dc4" }, "broom": { "Package": "broom", - 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"Version": "1.1.0", + "Version": "2.0.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "58baa74e4603fcfb9a94401c58c8f9b1" + "Hash": "a0bc51650201a56d00a4798523cc91b3" }, "mgcv": { "Package": "mgcv", - "Version": "1.8-31", + "Version": "1.8-36", + "Source": "Repository", + "Repository": "RSPM", + "Hash": "93cc747b0e1ad882a4570463c3575c23" + }, + "microbenchmark": { + "Package": "microbenchmark", + "Version": "1.4-7", "Source": "Repository", "Repository": "CRAN", - "Hash": "4bb7e0c4f3557583e1e8d3c9ffb8ba5c" + "Hash": "9ffe05828082333ec03d1669e5ca47b9" }, "mime": { "Package": "mime", - "Version": "0.9", + "Version": "0.11", "Source": "Repository", "Repository": "CRAN", - "Hash": "e87a35ec73b157552814869f45a63aa3" + "Hash": "8974a907200fc9948d636fe7d85ca9fb" }, "munsell": { "Package": "munsell", @@ -333,31 +382,24 @@ }, "nlme": { "Package": "nlme", - "Version": "3.1-147", + "Version": "3.1-152", "Source": "Repository", "Repository": "CRAN", - "Hash": "dd4a1423f1472a2a8e05dd2c8945c3af" + "Hash": "35de1ce639f20b5e10f7f46260730c65" }, "openssl": { "Package": "openssl", - 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"plyr": { - "Package": "plyr", - "Version": "1.8.6", + "Version": "1.2.1", "Source": "Repository", - "Repository": "CRAN", - "Hash": "ec0e5ab4e5f851f6ef32cd1d1984957f" + "Repository": "RSPM", + "Hash": "463642747f81879e6752485aefb831cf" }, "praise": { "Package": "praise", @@ -394,26 +429,19 @@ "Repository": "CRAN", "Hash": "a555924add98c99d2f411e37e7d25e9f" }, - "prettyunits": { - "Package": "prettyunits", - "Version": "1.1.1", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "95ef9167b75dde9d2ccc3c7528393e7e" - }, "processx": { "Package": "processx", - "Version": "3.4.2", + "Version": "3.5.2", "Source": "Repository", "Repository": "CRAN", - "Hash": "20a082f2bde0ffcd8755779fd476a274" + "Hash": "0cbca2bc4d16525d009c4dbba156b37c" }, "ps": { "Package": "ps", - "Version": "1.3.3", + "Version": "1.6.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "425d938eb9c02906a8ac98c0c2a306b5" + "Hash": "32620e2001c1dce1af49c49dccbb9420" }, "purrr": { "Package": "purrr", @@ -422,6 +450,13 @@ "Repository": "CRAN", "Hash": "97def703420c8ab10d8f0e6c72101e02" }, + "ragg": { + "Package": "ragg", + "Version": "1.1.3", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "663065eff8c6c08eaa9955c6f478ee7a" + }, "rematch2": { "Package": "rematch2", "Version": "2.1.2", @@ -429,47 +464,54 @@ "Repository": "CRAN", "Hash": "76c9e04c712a05848ae7a23d2f170a40" }, + "remotes": { + "Package": "remotes", + "Version": "2.4.0", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "a85ebb35721573b196317b49ddd2dfe4" + }, "renv": { "Package": "renv", - "Version": "0.11.0", + "Version": "0.14.0", "Source": "Repository", - "Repository": "RSPM", - "Hash": "1c3ef87cbb81c23ac96797781ec7aecc" + "Repository": "CRAN", + "Hash": "30e5eba91b67f7f4d75d31de14bbfbdc" }, - "reshape2": { - "Package": "reshape2", - "Version": "1.4.4", + "rex": { + "Package": "rex", + "Version": "1.2.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "bb5996d0bd962d214a11140d77589917" + "Hash": "093584b944440c5cd07a696b3c8e0e4c" }, "rlang": { "Package": "rlang", - "Version": "0.4.6", + "Version": "0.4.11", "Source": "Repository", - "Repository": "CRAN", - "Hash": "aa263e3ce17b177c49e0daade2ee3cdc" + "Repository": "RSPM", + "Hash": "515f341d3affe0de9e4a7f762efb0456" }, "rmarkdown": { "Package": "rmarkdown", - "Version": "2.2", + "Version": "2.10", "Source": "Repository", "Repository": "CRAN", - "Hash": "aa82ab08b203d5a32a5b5f404e80c399" + "Hash": "1fb097f233ee98968f8e5d0bcd42df6d" }, "rprojroot": { "Package": "rprojroot", - "Version": "1.3-2", + "Version": "2.0.2", "Source": "Repository", "Repository": "CRAN", - "Hash": "f6a407ae5dd21f6f80a6708bbb6eb3ae" + "Hash": "249d8cd1e74a8f6a26194a91b47f21d1" }, "rstudioapi": { "Package": "rstudioapi", - "Version": "0.11", + "Version": "0.13", "Source": "Repository", "Repository": "CRAN", - "Hash": "33a5b27a03da82ac4b1d43268f80088a" + "Hash": "06c85365a03fdaf699966cc1d3cf53ea" }, "scales": { "Package": "scales", @@ -480,10 +522,10 @@ }, "stringi": { "Package": "stringi", - "Version": "1.4.6", + "Version": "1.7.4", "Source": "Repository", "Repository": "CRAN", - "Hash": "e99d8d656980d2dd416a962ae55aec90" + "Hash": "ebaccb577da50829a3bb1b8296f318a5" }, "stringr": { "Package": "stringr", @@ -494,73 +536,94 @@ }, "sys": { "Package": "sys", - "Version": "3.3", + "Version": "3.4", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "b227d13e29222b4574486cfcbde077fa" + }, + "systemfonts": { + "Package": "systemfonts", + "Version": "1.0.2", "Source": "Repository", "Repository": "CRAN", - "Hash": "507f3116a38d37ad330a038b3be07b66" + "Hash": "f2e17ba09737e2e7e2ec40fc1f9b6e08" }, "testthat": { "Package": "testthat", - "Version": "2.3.2", + "Version": "3.0.4", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "575216c9946ca70016c3ffb9c31709ba" + }, + "textshaping": { + "Package": "textshaping", + "Version": "0.3.5", "Source": "Repository", "Repository": "CRAN", - "Hash": "0829b987b8961fb07f3b1b64a2fbc495" + "Hash": "6bb7e974b464fe72f192e51b4dab67f2" }, "tibble": { "Package": "tibble", - "Version": "3.0.1", + "Version": "3.1.4", "Source": "Repository", "Repository": "CRAN", - "Hash": "1c61e4cad000e03b1bd687db16a75926" + "Hash": "5e8ad5621e5c94b24ec07b88eee13df8" }, "tidyr": { "Package": "tidyr", - "Version": "1.1.0", + "Version": "1.1.3", "Source": "Repository", "Repository": "CRAN", - "Hash": "7395a05640bf91502dd475a84008d87e" + "Hash": "450d7dfaedde58e28586b854eeece4fa" }, "tidyselect": { "Package": "tidyselect", - "Version": "1.1.0", + "Version": "1.1.1", "Source": "Repository", "Repository": "CRAN", - "Hash": "6ea435c354e8448819627cf686f66e0a" + "Hash": "7243004a708d06d4716717fa1ff5b2fe" }, "tinytest": { "Package": "tinytest", - "Version": "1.2.2", + "Version": "1.3.1", "Source": "Repository", "Repository": "CRAN", - "Hash": "fb91119d9a21bf9897241fe74dad1a6b" + "Hash": "05d12b67ef20dd1043b4d4fd24d3cd73" }, "tinytex": { "Package": "tinytex", - "Version": "0.23", + "Version": "0.33", "Source": "Repository", "Repository": "CRAN", - "Hash": "a8c653ef8ce67683f588e6088ed45572" + "Hash": "6e0ad90ac5669e35d5456cb61b295acb" }, "utf8": { "Package": "utf8", - "Version": "1.1.4", + "Version": "1.2.2", "Source": "Repository", "Repository": "CRAN", - "Hash": "4a5081acfb7b81a572e4384a7aaf2af1" + "Hash": "c9c462b759a5cc844ae25b5942654d13" }, "vctrs": { "Package": "vctrs", - "Version": "0.3.1", + "Version": "0.3.8", "Source": "Repository", "Repository": "CRAN", - "Hash": "1739235995f08583db4095a28c357207" + "Hash": "ecf749a1b39ea72bd9b51b76292261f1" }, "viridisLite": { "Package": "viridisLite", + "Version": "0.4.0", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "55e157e2aa88161bdb0754218470d204" + }, + "waldo": { + "Package": "waldo", "Version": "0.3.0", "Source": "Repository", "Repository": "CRAN", - "Hash": "ce4f6271baa94776db692f1cb2055bee" + "Hash": "312b264fae22fdba83b7a74187a24da8" }, "whisker": { "Package": "whisker", @@ -571,17 +634,17 @@ }, "withr": { "Package": "withr", - "Version": "2.2.0", + "Version": "2.4.2", "Source": "Repository", "Repository": "CRAN", - "Hash": "ecd17882a0b4419545691e095b74ee89" + "Hash": "ad03909b44677f930fa156d47d7a3aeb" }, "xfun": { "Package": "xfun", - "Version": "0.14", + "Version": "0.25", "Source": "Repository", "Repository": "CRAN", - "Hash": "60e146e3aba37a547e12306dc3da77a6" + "Hash": "853d45ffff0a9af1e0af017cd359f75e" }, "xml2": { "Package": "xml2", @@ -590,6 +653,13 @@ "Repository": "CRAN", "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2" }, + "xmlparsedata": { + "Package": "xmlparsedata", + "Version": "1.0.5", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "45e4bf3c46476896e821fc0a408fb4fc" + }, "yaml": { "Package": "yaml", "Version": "2.2.1", diff --git a/vignettes/simTool.Rmd b/vignettes/simTool.Rmd index 7bc6d66..903b8fb 100644 --- a/vignettes/simTool.Rmd +++ b/vignettes/simTool.Rmd @@ -559,8 +559,8 @@ post_analyze <- function(o, .truth) { #post-process the object returned by boot.ci method = c("normal", "basic", "student", "percent", "bca") ret = o[method] - lower = unlist(purrr::map(ret, ~dplyr::nth(.x, -2))) - upper = sapply(ret, dplyr::last) + lower = unlist(purrr::map(ret, ~dplyr::nth(.x[1,], -2))) + upper = unlist(purrr::map(ret, ~dplyr::nth(.x[1,], -1))) type = paste("boot", method, sep = "_") return( @@ -657,8 +657,8 @@ bootstrap_ci <- function(x, conf.level, R = 999) { ci <- boot::boot.ci(b, conf = conf.level, type = "all") method = c("normal", "basic", "student", "percent", "bca") ret = ci[method] - lower = unlist(purrr::map(ret, ~dplyr::nth(.x, -2))) - upper = sapply(ret, dplyr::last) + lower = unlist(purrr::map(ret, ~dplyr::nth(.x[1,], -2))) + upper = unlist(purrr::map(ret, ~dplyr::nth(.x[1,], -1))) type = paste("boot", method, sep = "_") # unify return