README.md

In hoargroup/stationsweRegression: SWE Distribution for Station Observations

stationsweRegression

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!

Installation

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.

Windows

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)

Ubuntu

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)

Usage

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").

hoargroup/stationsweRegression documentation built on Feb. 4, 2024, 7:21 a.m.