inst/doc/presence-absence.R

In SDMtune: Species Distribution Model Selection

## ----knitr-options, include=FALSE---------------------------------------------
knitr::opts_chunk$set(comment = "#>",
                      collapse = TRUE,
                      eval = FALSE,
                      fig.align = "center")

## ----load-data----------------------------------------------------------------
# library(SDMtune)
# library(zeallot)
# 
# # Prepare data
# files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
#                     pattern = "grd",
#                     full.names = TRUE)
# 
# predictors <- terra::rast(files)
# p_coords <- virtualSp$presence
# a_coords <- virtualSp$absence
# data <- prepareSWD(species = "Virtual species",
#                    p = p_coords,
#                    a = a_coords,
#                    env = predictors[[1:8]])
# 
# # Split data in training and testing datasets
# c(train, test) %<-% trainValTest(data,
#                                  test = 0.2,
#                                  seed = 25)
# 
# cat("# Training  : ", nrow(train@data))
# cat("\n# Testing   : ", nrow(test@data))
# 
# # Create folds
# folds <- randomFolds(train,
#                      k = 4,
#                      seed = 25)

## ----ann----------------------------------------------------------------------
# set.seed(25)
# model <- train("ANN",
#                data = train,
#                size = 10,
#                folds = folds)
# 
# model

## ----auc----------------------------------------------------------------------
# auc(model)
# auc(model, test = TRUE)

## ----get-tunable-args---------------------------------------------------------
# getTunableArgs(model)

## ----optimize-model-----------------------------------------------------------
# h <- list(size = 10:50,
#           decay = c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5),
#           maxit = c(50, 100, 300, 500))
# 
# om <- optimizeModel(model,
#                     hypers = h,
#                     metric = "auc",
#                     seed = 25)

## ----best-model---------------------------------------------------------------
# best_model <- om@models[[1]]
# om@results[1, ]

## ----evaluate-final-model, fig.align='center'---------------------------------
# set.seed(25)
# final_model <- train("ANN",
#                      data = train,
#                      size = om@results[1, 1],
#                      decay = om@results[1, 2],
#                      maxit = om@results[1, 4])
# 
# plotROC(final_model,
#         test = test)