The function randomly permutes one variable at time (using training and absence/background datasets) and computes the decrease in training AUC. The result is normalized to percentages. Same implementation of MaxEnt java software but with the additional possibility of running several permutations to obtain a better estimate of the permutation importance. In case of more than one permutation (default is 10) the average of the decrease in training AUC is computed.
varImp(model, permut = 10)
SDMmodel or SDMmodelCV object.
integer. Number of permutations, default is 10.
Note that it could return values slightly different from MaxEnt Java software due to a different random permutation.
For SDMmodelCV objects the function returns the average and the standard deviation of the permutation importances of the single models.
data.frame with the ordered permutation importance.
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# Acquire environmental variables files <- list.files(path = file.path(system.file(package = "dismo"), "ex"), pattern = "grd", full.names = TRUE) predictors <- raster::stack(files) # Prepare presence and background locations p_coords <- virtualSp$presence bg_coords <- virtualSp$background # Create SWD object data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords, env = predictors, categorical = "biome") # Split presence locations in training (80%) and testing (20%) datasets datasets <- trainValTest(data, test = 0.2, only_presence = TRUE) train <- datasets[] test <- datasets[] # Train a model model <- train(method = "Maxnet", data = train, fc = "l") # Compute variable importance vi <- varImp(model, permut = 5) vi # Same example but using cross validation instead of training and testing # datasets # Create 4 random folds splitting only the presence locations folds = randomFolds(data, k = 4, only_presence = TRUE) model <- train(method = "Maxnet", data = data, fc = "l", folds = folds) # Compute variable importance vi <- varImp(model, permut = 5) vi
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