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))