# 4. Using blockCV with caret

#### Roozbeh Valavi

#### 2026-08-15

## Introduction

This tutorial shows how to use spatial cross-validation folds generated by `blockCV` in the `caret` modelling framework. The key step is to pass the training and testing row indices stored in `folds_list` to `caret::trainControl()` through its `index` and `indexOut` arguments.

## 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

## References

- Meyer H, Reudenbach C, Hengl T, Katurji M, Nauss T. 2018. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software 101: 1-9.

- Kuhn M. 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28: 1-26.

- 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