var-selection.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)
# data <- prepareSWD(species = "Virtual species",
#                    p = virtualSp$presence,
#                    a = virtualSp$background,
#                    env = predictors,
#                    categorical = "biome")
# 
# c(train, test) %<-% trainValTest(data,
#                                  test = 0.2,
#                                  only_presence = TRUE,
#                                  seed = 25)
# 
# # Train model
# model <- train("Maxent",
#                data = train)
# 
# # Train cross validation model
# folds <- randomFolds(data,
#                      k = 4,
#                      only_presence = TRUE,
#                      seed = 25)
# 
# cv_model <- train("Maxent",
#                   data = data,
#                   folds = folds)

## ----maxent-results-----------------------------------------------------------
# model@model@results

## ----maxent-var-importance----------------------------------------------------
# vi <- maxentVarImp(model)
# vi

## ----maxent-percent-contribution-plot-----------------------------------------
# plotVarImp(vi[, 1:2])

## ----maxent-permutation-importance-plot---------------------------------------
# plotVarImp(vi[, c(1,3)])

## ----maxnet-model-------------------------------------------------------------
# maxnet_model <- train("Maxnet",
#                       data = train)

## ----variable-importance------------------------------------------------------
# vi_maxnet <- varImp(maxnet_model,
#                     permut = 5)
# 
# vi_maxnet

## ----plot-var-imp-------------------------------------------------------------
# plotVarImp(vi_maxnet)

## ----permut-exercise----------------------------------------------------------
# # Compute the permutation importance
# vi_maxent <- varImp(model,
#                     permut = 10)
# 
# # Print it
# vi_maxent
# 
# # Compare with Maxent output
# maxentVarImp(model)

## ----jk-----------------------------------------------------------------------
# jk <- doJk(maxnet_model,
#            metric = "auc",
#            test = test)
# 
# jk

## ----plot-jk-train------------------------------------------------------------
# plotJk(jk,
#        type = "train",
#        ref = auc(maxnet_model))

## ----plot-jk-test-------------------------------------------------------------
# plotJk(jk,
#        type = "test",
#        ref = auc(maxnet_model, test = test))

## ----plot-bio1----------------------------------------------------------------
# plotResponse(maxnet_model,
#              var = "bio1",
#              type = "cloglog",
#              only_presence = TRUE,
#              marginal = FALSE,
#              rug = TRUE)

## ----plot-biome---------------------------------------------------------------
# plotResponse(maxnet_model,
#              var = "biome",
#              type = "logistic",
#              only_presence = TRUE,
#              marginal = TRUE,
#              fun = mean,
#              color = "blue")

## ----plot-cv-response---------------------------------------------------------
# plotResponse(cv_model,
#              var = "bio1",
#              type = "cloglog",
#              only_presence = TRUE,
#              marginal = TRUE,
#              fun = mean,
#              rug = TRUE)

## ----report-------------------------------------------------------------------
# modelReport(maxnet_model,
#             type = "cloglog",
#             folder = "virtual-sp",
#             test = test,
#             response_curves = TRUE,
#             only_presence = TRUE,
#             jk = TRUE,
#             env = predictors)

## ----backgrounds--------------------------------------------------------------
# set.seed(25)
# bg <- terra::spatSample(predictors,
#                         size = 10000,
#                         method = "random",
#                         na.rm = TRUE,
#                         xy = TRUE,
#                         values = FALSE)
# 
# bg <- prepareSWD(species = "Bgs",
#                  a = bg,
#                  env = predictors,
#                  categorical = "biome")

## ----heat-map-----------------------------------------------------------------
# plotCor(bg,
#         method = "spearman",
#         cor_th = 0.7)

## ----cor-var------------------------------------------------------------------
# corVar(bg,
#        method = "spearman",
#        cor_th = 0.7)

## ----varSel-------------------------------------------------------------------
# selected_variables_model <- varSel(maxnet_model,
#                                    metric = "auc",
#                                    test = test,
#                                    bg4cor = bg,
#                                    method = "spearman",
#                                    cor_th = 0.7,
#                                    permut = 1)

## ----output-varSel------------------------------------------------------------
# selected_variables_model

## ----exercise-1---------------------------------------------------------------
# selected_variables_model <- varSel(model,
#                                    metric = "aicc",
#                                    bg4cor = bg,
#                                    method = "spearman",
#                                    cor_th = 0.7,
#                                    env = predictors,
#                                    use_pc = TRUE)

## ----permutation--------------------------------------------------------------
# varImp(model,
#        permut = 1)

## ----reduce-var-1-------------------------------------------------------------
# cat("Testing AUC before: ", auc(maxnet_model, test = test))
# 
# reduced_variables_model <- reduceVar(maxnet_model,
#                                      th = 6,
#                                      metric = "auc",
#                                      test = test,
#                                      permut = 1)
# 
# cat("Testing AUC after: ", auc(reduced_variables_model, test = test))

## ----reduce-var-2-------------------------------------------------------------
# cat("Testing AUC before: ", auc(maxnet_model, test = test))
# 
# reduced_variables_model <- reduceVar(maxnet_model,
#                                      th = 15, metric = "auc",
#                                      test = test,
#                                      permut = 1,
#                                      use_jk = TRUE)
# 
# cat("Testing AUC after: ", auc(reduced_variables_model, test = test))