The original purpose of this package was written to estimate SWE distribution using linear regression following the methods outlined in Schneider and Molotch, 2016 with some minor differences:
fsca is used as a predictor instead of reconstructed SWE. If you look in the discussion of the paper, we show that this is almost as good as using reconstructed SWE. I've done some more tests and found that the accuracy trade off versus not having to run the reconstruction model for new domains is worthwhile for my immediate purposes.
As of v0.2 reconstructed swe can be used to estimate SWE using flag SNOW_VAR='rcn'. See the updated runfiles in the corresponding example_sweregression repo.
- the statistical model from the paper was upgraded from a step-wise linear regression to an elastic-net linear regression. in short, this means that all the predictor variables get used rather than dropping the variables with the lowest predictive ability or because of multicollinearity.
NB v0.3 provided the ability to simulate SWE in California using CDEC stations!
install with devtools::install_github("hoargroup/stationsweRegression", build_vignettes = TRUE)
See notes below for setting up your R environment before the running the above command in R.
Install R if you haven't: https://cran.r-project.org -> Download R for Windows -> base -> install .exe
Install Rtools to be able to compile (not sure if this is needed as just a user): https://cran.r-project.org -> Download R for Windows -> Rtools -> follow instructions
Install RStudio for your R interface https://www.rstudio.com/products/rstudio/download/#download
In R:
install.packages(c("installr","devtools"))
library(installr)
install.pandoc()
devtools::install_github("hoargroup/stationsweRegression", build_vignettes = TRUE)
Install R if you haven't: https://cran.r-project.org -> Download R for Linux -> Ubuntu (but pick your distribution if not ubuntu) -> follow instructions
commandline:
add ubuntugis rep
sudo add-apt-repository ppa:ubuntugis/ppa
sudo apt-get update
Install gdal
sudo apt install libgdal-dev
sudo apt install --no-install-recommends r-base r-cran-devtools libcurl4-openssl-dev pandoc r-cran-raster r-cran-tidyverse r-cran-ncdf4
Install RStudio for your R interface https://www.rstudio.com/products/rstudio/download/#download
Then open R and install
install.packages(c('gdalUtils','glmnetUtils','rgdal'))
devtools::install_github("hoargroup/stationsweRegression", build_vignettes = TRUE)
Please read the vignettes for details regarding use of the package. These can be read from this github repository or from R with browseVignettes("stationsweRegression").