Prepare the data
Load the simulated presence-absence data and environmental raster
covariates included with blockCV.
library(blockCV)
library(sf)
library(terra)
# import presence-absence species data
points <- read.csv(system.file("extdata/", "species.csv", package = "blockCV"))
# make an sf object from the data.frame
pa_data <- sf::st_as_sf(points, coords = c("x", "y"), crs = 7845)
# load raster covariates
covars <- terra::rast(
list.files(system.file("extdata/au/", package = "blockCV"), full.names = TRUE)
)
Extract the raster covariate values at the species records. This is
the modelling table that will be supplied to caret.
training <- terra::extract(covars, pa_data, ID = FALSE)
training$occ <- as.factor(pa_data$occ)
head(training)
## bio_12 bio_15 bio_4 bio_5 occ
## 1 1287.2784 93.80042 323.8961 31.22999 0
## 2 1114.8223 118.56091 240.8846 29.44557 0
## 3 958.7523 85.10988 346.9273 28.04835 1
## 4 610.1941 22.76476 559.0266 31.00196 1
## 5 553.3838 15.44879 584.8892 31.40681 0
## 6 1729.3408 113.51132 123.3365 32.81274 0
Create spatial folds
Create spatial blocks with cv_spatial(). Each item in
folds_list contains two integer vectors: the first is the
training row indices and the second is the held-out testing row
indices.
set.seed(123)
sb1 <- cv_spatial(
x = pa_data,
column = "occ",
r = covars,
size = 450000,
k = 5,
selection = "random",
iteration = 50,
progress = FALSE,
report = TRUE,
plot = TRUE
)
##
## train_0 train_1 test_0 test_1
## 1 198 223 59 20
## 2 203 183 54 60
## 3 204 197 53 46
## 4 213 196 44 47
## 5 210 173 47 70
Fit a caret model
The only conversion needed for caret is to separate the
training and testing indices from folds_list. The following
example uses a random forest model through caret::train().
The modelling chunk is not evaluated when the vignette is built because
caret and randomForest are optional modelling
packages.
library(caret)
train_index <- lapply(sb1$folds_list, function(fold) fold[[1]])
test_index <- lapply(sb1$folds_list, function(fold) fold[[2]])
names(train_index) <- paste0("Fold", seq_along(train_index))
names(test_index) <- names(train_index)
control <- trainControl(
method = "cv",
number = length(train_index),
index = train_index,
indexOut = test_index,
search = "random"
)
set.seed(123)
rf_model <- train(
occ ~ .,
data = training,
method = "rf",
trControl = control,
tuneLength = 4,
ntree = 500
)
rf_model
rf_model$resample
plot(rf_model)
The resampling results are now based on the spatial folds from
blockCV, rather than on random non-spatial folds generated
internally by caret.
The CAST
package uses the same caret interface. Its
CreateSpacetimeFolds() function creates train and test
lists from pre-defined spatial, temporal, or spatio-temporal groups that
can be passed directly to
trainControl(index = ..., indexOut = ...).
Please cite blockCV by: Valavi R, Elith J,
Lahoz-Monfort JJ, Guillera-Arroita G. blockCV: An R package for
generating spatially or environmentally separated folds for k-fold
cross-validation of species distribution models. Methods Ecol Evol.
2019; 10:225-232. doi:
10.1111/2041-210X.13107