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The goal of GWlasso is to provides a set of functions to perform Geographically weighted lasso. It was originally thought to be used in palaeoecological settings but can be used to other extents.
The package has been submitted to CRAN and is awaiting evaluation
You can install the development version of GWlasso from GitHub with:
# install.packages("devtools")
::install_github("nibortolum/GWlasso") devtools
This is a basic example on how to run a GWlasso pipeline:
library(GWlasso)
## compute a distance matrix from a set of coordinates
<- compute_distance_matrix <- function(coords, method = "euclidean", add.noise = FALSE)
distance_matrix
## compute the optimal bandwidth
<- gwl_bw_estimation(x.var = predictors_df,
myst.est y.var = y_vector,
dist.mat = distance_matrix,
adaptive = TRUE,
adptbwd.thresh = 0.1,
kernel = "bisquare",
alpha = 1,
progress = TRUE,
n=40,
nfolds = 5)
## Compute the optimal model
<- gwl_fit(myst.est$bw,
my.gwl.fit x.var = data.sample[,-1],
y.var = data.sample$WTD,
kernel = "bisquare",
dist.mat = distance_matrix,
alpha = 1,
adaptive = TRUE, progress = T)
## make predictions
<- predict(my.gwl.fit, newdata = new_data, newcoords = new_coords) predicted_values
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.