Package 'mlr3spatiotempcv'

Description

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at doi:10.18637/jss.v111.i07.

Main resources

• Book on mlr3: https://mlr3book.mlr-org.com

• mlr3book section about spatiotemporal data: https://mlr3book.mlr-org.com/chapters/chapter13/beyond_regression_and_classification.html#spatiotemp-cv

• package vignettes: https://mlr3spatiotempcv.mlr-org.com/dev/articles/

Miscellaneous mlr3 content

• Use cases and examples: https://mlr3gallery.mlr-org.com

• More classification and regression tasks: mlr3data

• Connector to OpenML: mlr3oml

• More classification and regression learners: mlr3learners

• Even more learners: https://github.com/mlr-org/mlr3extralearners

• Preprocessing and machine learning pipelines: mlr3pipelines

• Tuning of hyperparameters: mlr3tuning

• Visualizations for many mlr3 objects: mlr3viz

• Survival analysis and probabilistic regression: mlr3proba

• Cluster analysis: mlr3cluster

• Feature selection filters: mlr3filters

• Feature selection wrappers: mlr3fselect

• Interface to real (out-of-memory) data bases: mlr3db

• Performance measures as plain functions: mlr3measures

• Parallelization framework: future

• Progress bars: progressr

Author(s)

Maintainer: Patrick Schratz [email protected] (ORCID)

Authors:

• Marc Becker [email protected] (ORCID)

Other contributors:

• Jannes Muenchow [email protected] (ORCID) [contributor]

• Michel Lang [email protected] (ORCID) [contributor]

References

Schratz P, Muenchow J, Iturritxa E, Richter J, Brenning A (2019). “Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data.” Ecological Modelling, 406, 109–120. doi:10.1016/j.ecolmodel.2019.06.002.

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

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. doi:10.1016/j.envsoft.2017.12.001.

Zhao Y, Karypis G (2002). “Evaluation of Hierarchical Clustering Algorithms for Document Datasets.” 11th Conference of Information and Knowledge Management (CIKM), 51-524. doi:10.1145/584792.584877.

See Also

Useful links:

• https://mlr3spatiotempcv.mlr-org.com/

• https://github.com/mlr-org/mlr3spatiotempcv

• https://mlr3book.mlr-org.com

• Report bugs at https://github.com/mlr-org/mlr3spatiotempcv/issues

Description

Convert an object to a TaskClassifST. This is a S3 generic for the following objects:

1. TaskClassifST: Ensure the identity.

2. data.frame() and mlr3::DataBackend: Provides an alternative to the constructor of TaskClassifST.

3. sf::sf: Extracts spatial meta data before construction.

4. mlr3::TaskRegr: Calls mlr3::convert_task().

Usage

as_task_classif_st(x, ...)

## S3 method for class 'TaskClassifST'
as_task_classif_st(x, clone = FALSE, ...)

## S3 method for class 'data.frame'
as_task_classif_st(
  x,
  target,
  id = deparse(substitute(x)),
  positive = NULL,
  coordinate_names,
  crs = NA_character_,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

## S3 method for class 'DataBackend'
as_task_classif_st(
  x,
  target,
  id = deparse(substitute(x)),
  positive = NULL,
  coordinate_names,
  crs,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

## S3 method for class 'sf'
as_task_classif_st(
  x,
  target = NULL,
  id = deparse(substitute(x)),
  positive = NULL,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

Arguments

Value

TaskClassifST.

Examples

if (mlr3misc::require_namespaces(c("sf"), quietly = TRUE)) {
  library("mlr3")
  data("ecuador", package = "mlr3spatiotempcv")

  # data.frame
  as_task_classif_st(ecuador, target = "slides", positive = "TRUE",
    coords_as_features = FALSE,
    crs = "+proj=utm +zone=17 +south +datum=WGS84 +units=m +no_defs",
    coordinate_names = c("x", "y"))

  # sf
  ecuador_sf = sf::st_as_sf(ecuador, coords = c("x", "y"), crs = 32717)
  as_task_classif_st(ecuador_sf, target = "slides", positive = "TRUE")
}

Description

Convert object to a TaskRegrST.

This is a S3 generic, specialized for at least the following objects:

1. TaskRegrST: Ensure the identity.

2. data.frame() and mlr3::DataBackend: Provides an alternative to the constructor of TaskRegrST.

3. sf::sf: Extracts spatial meta data before construction.

4. mlr3::TaskClassif: Calls mlr3::convert_task().

Usage

## S3 method for class 'TaskClassifST'
as_task_regr_st(
  x,
  target = NULL,
  drop_original_target = FALSE,
  drop_levels = TRUE,
  ...
)

as_task_regr_st(x, ...)

## S3 method for class 'TaskRegrST'
as_task_regr_st(x, clone = FALSE, ...)

## S3 method for class 'data.frame'
as_task_regr_st(
  x,
  target,
  id = deparse(substitute(x)),
  coordinate_names,
  crs = NA_character_,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

## S3 method for class 'DataBackend'
as_task_regr_st(
  x,
  target,
  id = deparse(substitute(x)),
  positive = NULL,
  coordinate_names,
  crs,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

## S3 method for class 'sf'
as_task_regr_st(
  x,
  target = NULL,
  id = deparse(substitute(x)),
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

## S3 method for class 'TaskClassifST'
as_task_regr_st(
  x,
  target = NULL,
  drop_original_target = FALSE,
  drop_levels = TRUE,
  ...
)

Arguments

Value

TaskRegrST

Examples

if (mlr3misc::require_namespaces(c("sf"), quietly = TRUE)) {
  library("mlr3")
  data("cookfarm_mlr3", package = "mlr3spatiotempcv")

  # data.frame
  as_task_regr_st(cookfarm_mlr3, target = "PHIHOX",
    coords_as_features = FALSE, crs = 26911,
    coordinate_names = c("x", "y"))

  # sf
  cookfarm_sf = sf::st_as_sf(cookfarm_mlr3, coords = c("x", "y"), crs = 26911)
  as_task_regr_st(cookfarm_sf, target = "PHIHOX")
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods.

Usage

## S3 method for class 'ResamplingCustomCV'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingCustomCV'
plot(x, ...)

Arguments

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf", "patchwork"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  breaks = quantile(task$data()$dem, seq(0, 1, length = 6))
  zclass = cut(task$data()$dem, breaks, include.lowest = TRUE)

  resampling = rsmp("custom_cv")
  resampling$instantiate(task, f = zclass)

  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
  autoplot(resampling, task, fold_id = 1)
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods.

Usage

## S3 method for class 'ResamplingCV'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedCV'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingCV'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedCV'
plot(x, ...)

Arguments

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf", "patchwork", "ggtext", "ggsci"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("cv")
  resampling$instantiate(task)

  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
  autoplot(resampling, task, fold_id = 1)
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSpCVBlock'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_blocks = FALSE,
  show_labels = FALSE,
  sample_fold_n = NULL,
  label_size = 2,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVBlock'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_blocks = FALSE,
  show_labels = FALSE,
  sample_fold_n = NULL,
  label_size = 2,
  ...
)

## S3 method for class 'ResamplingSpCVBlock'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVBlock'
plot(x, ...)

Arguments

Details

By default a plot is returned; if fold_id is set, a gridded plot is created. If plot_as_grid = FALSE, a list of plot objects is returned. This can be used to align the plots individually.

When no single fold is selected, the ggsci::scale_color_ucscgb() palette is used to display all partitions. If you want to change the colors, call ⁠<plot> + <color-palette>()⁠.

Value

ggplot2::ggplot() or list of ggplot2 objects.

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_block", range = 1000L)
  resampling$instantiate(task)

  ## list of ggplot2 resamplings
  plot_list = autoplot(resampling, task,
    crs = 4326,
    fold_id = c(1, 2), plot_as_grid = FALSE)

  ## Visualize all partitions
  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## Visualize the train/test split of a single fold
  autoplot(resampling, task, fold_id = 1) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## Visualize train/test splits of multiple folds
  autoplot(resampling, task,
    fold_id = c(1, 2),
    show_blocks = TRUE) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSpCVBuffer'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_omitted = FALSE,
  ...
)

## S3 method for class 'ResamplingSpCVBuffer'
plot(x, ...)

Arguments

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_buffer", theRange = 1000)
  resampling$instantiate(task)

  ## single fold
  autoplot(resampling, task, fold_id = 1) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## multiple folds
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods.

Usage

## S3 method for class 'ResamplingSpCVCoords'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVCoords'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingSpCVCoords'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVCoords'
plot(x, ...)

Arguments

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_coords")
  resampling$instantiate(task)

  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
  autoplot(resampling, task, fold_id = 1)
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSpCVDisc'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  repeats_id = NULL,
  show_omitted = FALSE,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVDisc'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_omitted = FALSE,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingSpCVDisc'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVDisc'
plot(x, ...)

Arguments

Details

This method requires to set argument fold_id and no plot containing all partitions can be created. This is because the method does not make use of all observations but only a subset of them (many observations are left out). Hence, train and test sets of one fold are not re-used in other folds as in other methods and plotting these without a train/test indicator would not make sense.

2D vs 3D plotting

This method has both a 2D and a 3D plotting method. The 2D method returns a ggplot with x and y axes representing the spatial coordinates. The 3D method uses plotly to create an interactive 3D plot. Set plot3D = TRUE to use the 3D method.

Note that spatiotemporal datasets usually suffer from overplotting in 2D mode.

See Also

• mlr3book chapter on "Spatial Analysis"

• Vignette Spatiotemporal Visualization.

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

Examples

if (mlr3misc::require_namespaces("sf", quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_disc",
    folds = 5, radius = 200L, buffer = 200L)
  resampling$instantiate(task)

  autoplot(resampling, task,
    fold_id = 1,
    show_omitted = TRUE, size = 0.7) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods.

Usage

## S3 method for class 'ResamplingSpCVEnv'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVEnv'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingSpCVEnv'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVEnv'
plot(x, ...)

Arguments

See Also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_env", folds = 4, features = "dem")
  resampling$instantiate(task)

  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
  autoplot(resampling, task, fold_id = 1)
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSpCVKnndm'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  repeats_id = NULL,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVKnndm'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingSpCVKnndm'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVKnndm'
plot(x, ...)

Arguments

Details

This method requires to set argument fold_id and no plot containing all partitions can be created. This is because the method does not make use of all observations but only a subset of them (many observations are left out). Hence, train and test sets of one fold are not re-used in other folds as in other methods and plotting these without a train/test indicator would not make sense.

2D vs 3D plotting

This method has both a 2D and a 3D plotting method. The 2D method returns a ggplot with x and y axes representing the spatial coordinates. The 3D method uses plotly to create an interactive 3D plot. Set plot3D = TRUE to use the 3D method.

Note that spatiotemporal datasets usually suffer from overplotting in 2D mode.

See Also

• mlr3book chapter on "Spatial Analysis"

• Vignette Spatiotemporal Visualization.

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

Examples

if (mlr3misc::require_namespaces(c("CAST", "sf"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  points = sf::st_as_sf(task$coordinates(), crs = task$crs, coords = c("x", "y"))
  modeldomain = sf::st_as_sfc(sf::st_bbox(points))

  resampling = rsmp("spcv_knndm",
    folds = 5, modeldomain = modeldomain)
  resampling$instantiate(task)

  autoplot(resampling, task,
    fold_id = 1, size = 0.7) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSpCVTiles'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  repeats_id = NULL,
  show_omitted = FALSE,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVTiles'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_omitted = FALSE,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingSpCVTiles'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSpCVTiles'
plot(x, ...)

Arguments

Details

Specific combinations of arguments of "spcv_tiles" remove some observations, hence show_omitted has an effect in some cases.

See Also

• mlr3book chapter on "Spatial Analysis"

• Vignette Spatiotemporal Visualization.

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

Examples

if (mlr3misc::require_namespaces(c("sf", "sperrorest"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_tiles",
    nsplit = c(4L, 3L), reassign = FALSE)
  resampling$instantiate(task)

  autoplot(resampling, task,
    fold_id = 1,
    show_omitted = TRUE, size = 0.7) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

Description

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

Usage

## S3 method for class 'ResamplingSptCVCstf'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  repeats_id = NULL,
  tickformat_date = "%Y-%m",
  nticks_x = 3,
  nticks_y = 3,
  point_size = 3,
  axis_label_fontsize = 11,
  static_image = FALSE,
  show_omitted = FALSE,
  plot3D = NULL,
  plot_time_var = NULL,
  sample_fold_n = NULL,
  ...
)

## S3 method for class 'ResamplingRepeatedSptCVCstf'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  tickformat_date = "%Y-%m",
  nticks_x = 3,
  nticks_y = 3,
  point_size = 3,
  axis_label_fontsize = 11,
  plot3D = NULL,
  plot_time_var = NULL,
  ...
)

## S3 method for class 'ResamplingSptCVCstf'
plot(x, ...)

## S3 method for class 'ResamplingRepeatedSptCVCstf'
plot(x, ...)

Arguments

Details

This method requires to set argument fold_id. No plot showing all folds in one plot can be created. This is because the LLTO method does not make use of all observations but only a subset of them (many observations are omitted). Hence, train and test sets of one fold are not re-used in other folds as in other methods and plotting these without a train/test indicator would be misleading.

2D vs 3D plotting

This method has both a 2D and a 3D plotting method. The 2D method returns a ggplot with x and y axes representing the spatial coordinates. The 3D method uses plotly to create an interactive 3D plot. Set plot3D = TRUE to use the 3D method.

Note that spatiotemporal datasets usually suffer from overplotting in 2D mode.

See Also

• mlr3book chapter on "Spatiotemporal Visualization"

• Vignette Spatiotemporal Visualization.

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

Examples

if (mlr3misc::require_namespaces(c("sf", "plotly"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task_st = tsk("cookfarm_mlr3")
  task_st$set_col_roles("SOURCEID", "space")
  task_st$set_col_roles("Date", "time")
  resampling = rsmp("sptcv_cstf", folds = 5)
  resampling$instantiate(task_st)

  # with both `"space"` and `"time"` column roles set (LLTO), the omitted
  # observations per fold can be shown by setting `show_omitted = TRUE`
  autoplot(resampling, task_st, fold_id = 1, show_omitted = TRUE)
}

Description

This function creates spatially separated folds based on a distance to number of row and/or column. It assigns blocks to the training and testing folds randomly, systematically or in a checkerboard pattern. The distance (size) should be in metres, regardless of the unit of the reference system of the input data (for more information see the details section). By default, the function creates blocks according to the extent and shape of the spatial sample data (x e.g. the species occurrence), Alternatively, blocks can be created based on r assuming that the user has considered the landscape for the given species and case study. Blocks can also be offset so the origin is not at the outer corner of the rasters. Instead of providing a distance, the blocks can also be created by specifying a number of rows and/or columns and divide the study area into vertical or horizontal bins, as presented in Wenger & Olden (2012) and Bahn & McGill (2012). Finally, the blocks can be specified by a user-defined spatial polygon layer.

Details

To maintain consistency, all functions in this package use meters as their unit of measurement. However, when the input map has a geographic coordinate system (in decimal degrees), the block size is calculated by dividing the size parameter by deg_to_metre (which defaults to 111325 meters, the standard distance of one degree of latitude on the Equator). In reality, this value varies by a factor of the cosine of the latitude. So, an alternative sensible value could be cos(mean(sf::st_bbox(x)[c(2,4)]) * pi/180) * 111325.

The offset can be used to change the spatial position of the blocks. It can also be used to assess the sensitivity of analysis results to shifting in the blocking arrangements. These options are available when size is defined. By default the region is located in the middle of the blocks and by setting the offsets, the blocks will shift.

Roberts et. al. (2017) suggest that blocks should be substantially bigger than the range of spatial autocorrelation (in model residual) to obtain realistic error estimates, while a buffer with the size of the spatial autocorrelation range would result in a good estimation of error. This is because of the so-called edge effect (O'Sullivan & Unwin, 2014), whereby points located on the edges of the blocks of opposite sets are not separated spatially. Blocking with a buffering strategy overcomes this issue (see cv_buffer).

mlr3spatiotempcv notes

By default blockCV::cv_spatial() does not allow the creation of multiple repetitions. mlr3spatiotempcv adds support for this when using the size argument for fold creation. When supplying a vector of length(repeats) for argument size, these different settings will be used to create folds which differ among the repetitions.

Multiple repetitions are not possible when using the "row & cols" approach because the created folds will always be the same.

The 'Description' and 'Details' fields are inherited from the respective upstream function.

For a list of available arguments, please see blockCV::cv_spatial.

blockCV >= 3.0.0 changed the argument names of the implementation. For backward compatibility, mlr3spatiotempcv is still using the old ones. Here's a list which shows the mapping between blockCV < 3.0.0 and blockCV >= 3.0.0:

• range -> size

• rasterLayer -> r

• speciesData -> points

• showBlocks -> plot

• cols and rows -> rows_cols

The default of argument hexagon is different in mlr3spatiotempcv (FALSE instead of TRUE) to create square blocks instead of hexagonal blocks by default.

Parameters

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVBlock

Public fields

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVBlock$new()

• ResamplingRepeatedSpCVBlock$folds()

• ResamplingRepeatedSpCVBlock$repeats()

• ResamplingRepeatedSpCVBlock$instantiate()

• ResamplingRepeatedSpCVBlock$clone()

Method new()

Create an "spatial block" repeated resampling instance.

For a list of available arguments, please see blockCV::cv_spatial.

Usage

ResamplingRepeatedSpCVBlock$new(id = "repeated_spcv_block")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVBlock$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVBlock$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVBlock$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVBlock$clone(deep = FALSE)

Arguments

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

Examples

## Not run: 
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("diplodia")

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_block",
    folds = 3, repeats = 2,
    range = c(5000L, 10000L))
  rrcv$instantiate(task)

  # Individual sets:
  rrcv$iters
  rrcv$folds(1:6)
  rrcv$repeats(1:6)

  # Individual sets:
  rrcv$train_set(1)
  rrcv$test_set(1)
  intersect(rrcv$train_set(1), rrcv$test_set(1))

  # Internal storage:
  rrcv$instance # table
}

## End(Not run)

Description

Splits data by clustering in the coordinate space. See the upstream implementation at sperrorest::partition_kmeans() and Brenning (2012) for further information.

Details

Universal partitioning method that splits the data in the coordinate space. Useful for spatially homogeneous datasets that cannot be split well with rectangular approaches like ResamplingSpCVBlock.

Parameters

• folds (integer(1))
  Number of folds.

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVCoords

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVCoords$new()

• ResamplingRepeatedSpCVCoords$folds()

• ResamplingRepeatedSpCVCoords$repeats()

• ResamplingRepeatedSpCVCoords$instantiate()

• ResamplingRepeatedSpCVCoords$clone()

Method new()

Create an "coordinate-based" repeated resampling instance.

For a list of available arguments, please see sperrorest::partition_cv.

Usage

ResamplingRepeatedSpCVCoords$new(id = "repeated_spcv_coords")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVCoords$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVCoords$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVCoords$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVCoords$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

Examples

library(mlr3)
task = tsk("diplodia")

# Instantiate Resampling
rrcv = rsmp("repeated_spcv_coords", folds = 3, repeats = 5)
rrcv$instantiate(task)

# Individual sets:
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))

# Internal storage:
rrcv$instance # table

Description

(sperrorest) Repeated spatial "disc" resampling

(sperrorest) Repeated spatial "disc" resampling

Parameters

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVDisc

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVDisc$new()

• ResamplingRepeatedSpCVDisc$folds()

• ResamplingRepeatedSpCVDisc$repeats()

• ResamplingRepeatedSpCVDisc$instantiate()

• ResamplingRepeatedSpCVDisc$clone()

Method new()

Create a "Spatial 'Disc' resampling" resampling instance.

For a list of available arguments, please see sperrorest::partition_disc.

Usage

ResamplingRepeatedSpCVDisc$new(id = "repeated_spcv_disc")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVDisc$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVDisc$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVDisc$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVDisc$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

Examples

library(mlr3)
task = tsk("ecuador")

# Instantiate Resampling
rrcv = rsmp("repeated_spcv_disc",
  folds = 3L, repeats = 2,
  radius = 200L, buffer = 200L)
rrcv$instantiate(task)

# Individual sets:
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))

# Internal storage:
rrcv$instance # table

Description

Splits data by clustering in the feature space. See the upstream implementation at blockCV::cv_cluster() and Valavi et al. (2018) for further information.

Details

Useful when the dataset is supposed to be split on environmental information which is present in features. The method allows for a combination of multiple features for clustering.

The input of raster images directly as in blockCV::cv_cluster() is not supported. See mlr3spatial and its raster DataBackends for such support in mlr3.

Parameters

• folds (integer(1))
  Number of folds.

• features (character())
  The features to use for clustering.

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVEnv

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVEnv$new()

• ResamplingRepeatedSpCVEnv$folds()

• ResamplingRepeatedSpCVEnv$repeats()

• ResamplingRepeatedSpCVEnv$instantiate()

• ResamplingRepeatedSpCVEnv$clone()

Method new()

Create an "Environmental Block" repeated resampling instance.

For a list of available arguments, please see blockCV::cv_cluster.

Usage

ResamplingRepeatedSpCVEnv$new(id = "repeated_spcv_env")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVEnv$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVEnv$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVEnv$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVEnv$clone(deep = FALSE)

Arguments

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_env", folds = 4, repeats = 2)
  rrcv$instantiate(task)

  # Individual sets:
  rrcv$train_set(1)
  rrcv$test_set(1)
  intersect(rrcv$train_set(1), rrcv$test_set(1))

  # Internal storage:
  rrcv$instance
}

Description

This function implements the kNNDM algorithm and returns the necessary indices to perform a k-fold NNDM CV for map validation.

Details

knndm is a k-fold version of NNDM LOO CV for medium and large datasets. Brielfy, the algorithm tries to find a k-fold configuration such that the integral of the absolute differences (Wasserstein W statistic) between the empirical nearest neighbour distance distribution function between the test and training data during CV (Gj*), and the empirical nearest neighbour distance distribution function between the prediction and training points (Gij), is minimised. It does so by performing clustering of the training points' coordinates for different numbers of clusters that range from k to N (number of observations), merging them into k final folds, and selecting the configuration with the lowest W.

Using a projected CRS in 'knndm' has large computational advantages since fast nearest neighbour search can be done via the 'FNN' package, while working with geographic coordinates requires computing the full spherical distance matrices. As a clustering algorithm, 'kmeans' can only be used for projected CRS while 'hierarchical' can work with both projected and geographical coordinates, though it requires calculating the full distance matrix of the training points even for a projected CRS.

In order to select between clustering algorithms and number of folds 'k', different 'knndm' configurations can be run and compared, being the one with a lower W statistic the one that offers a better match. W statistics between 'knndm' runs are comparable as long as 'tpoints' and 'predpoints' or 'modeldomain' stay the same.

Map validation using 'knndm' should be used using 'CAST::global_validation', i.e. by stacking all out-of-sample predictions and evaluating them all at once. The reasons behind this are 1) The resulting folds can be unbalanced and 2) nearest neighbour functions are constructed and matched using all CV folds simultaneously.

If training data points are very clustered with respect to the prediction area and the presented 'knndm' configuration still show signs of Gj* > Gij, there are several things that can be tried. First, increase the 'maxp' parameter; this may help to control for strong clustering (at the cost of having unbalanced folds). Secondly, decrease the number of final folds 'k', which may help to have larger clusters.

The 'modeldomain' is either a sf polygon that defines the prediction area, or alternatively a SpatRaster out of which a polygon, transformed into the CRS of the training points, is defined as the outline of all non-NA cells. Then, the function takes a regular point sample (amount defined by 'samplesize') from the spatial extent. As an alternative use 'predpoints' instead of 'modeldomain', if you have already defined the prediction locations (e.g. raster pixel centroids). When using either 'modeldomain' or 'predpoints', we advise to plot the study area polygon and the training/prediction points as a previous step to ensure they are aligned.

'knndm' can also be performed in the feature space by setting 'space' to "feature". Euclidean distances or Mahalanobis distances can be used for distance calculation, but only Euclidean are tested. In this case, nearest neighbour distances are calculated in n-dimensional feature space rather than in geographical space. 'tpoints' and 'predpoints' can be data frames or sf objects containing the values of the features. Note that the names of 'tpoints' and 'predpoints' must be the same. 'predpoints' can also be missing, if 'modeldomain' is of class SpatRaster. In this case, the values of of the SpatRaster will be extracted to the 'predpoints'. In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances [Experimental]. If categorical features are present, and 'clustering' = "kmeans", K-Prototype clustering will be performed instead.

Parameters

• folds (integer(1))
  Number of folds.

• stratify
  If TRUE, stratify on the target column.

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVKnndm

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVKnndm$new()

• ResamplingRepeatedSpCVKnndm$folds()

• ResamplingRepeatedSpCVKnndm$repeats()

• ResamplingRepeatedSpCVKnndm$instantiate()

• ResamplingRepeatedSpCVKnndm$clone()

Method new()

Create a "K-fold Nearest Neighbour Distance Matching" resampling instance.

Usage

ResamplingRepeatedSpCVKnndm$new(id = "repeated_spcv_knndm")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVKnndm$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVKnndm$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVKnndm$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVKnndm$clone(deep = FALSE)

Arguments

References

Linnenbrink, J., Mila, C., Ludwig, M., Meyer, H. (2023). “kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation.” EGUsphere, 2023, 1–16. doi:10.5194/egusphere-2023-1308, https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1308/.

Examples

library(mlr3)
library(mlr3spatial)
set.seed(42)
simarea = list(matrix(c(0, 0, 0, 100, 100, 100, 100, 0, 0, 0), ncol = 2, byrow = TRUE))
simarea = sf::st_polygon(simarea)
train_points = sf::st_sample(simarea, 1000, type = "random")
train_points = sf::st_as_sf(train_points)
train_points$target = as.factor(sample(c("TRUE", "FALSE"), 1000, replace = TRUE))
pred_points = sf::st_sample(simarea, 1000, type = "regular")

task = mlr3spatial::as_task_classif_st(sf::st_as_sf(train_points), "target", positive = "TRUE")

cv_knndm = rsmp("repeated_spcv_knndm", predpoints = pred_points, repeats = 2)
cv_knndm$instantiate(task)
#' ### Individual sets:
# cv_knndm$train_set(1)
# cv_knndm$test_set(1)
# check that no obs are in both sets
intersect(cv_knndm$train_set(1), cv_knndm$test_set(1)) # good!

# Internal storage:
# cv_knndm$instance # table

Description

Spatial partitioning using rectangular tiles. Small partitions can optionally be merged into adjacent ones to avoid partitions with too few observations. This method is similar to ResamplingSpCVBlock by making use of rectangular zones in the coordinate space. See the upstream implementation at sperrorest::partition_disc() and Brenning (2012) for further information.

Parameters

• dsplit (integer(2))
  Equidistance of splits in (possibly rotated) x direction (dsplit[1]) and y direction (dsplit[2]) used to define tiles. If dsplit is of length 1, its value is recycled. Either dsplit or nsplit must be specified.

• nsplit (integer(2))
  Number of splits in (possibly rotated) x direction (nsplit[1]) and y direction (nsplit[2]) used to define tiles. If nsplit is of length 1, its value is recycled.

• rotation (character(1))
  Whether and how the rectangular grid should be rotated; random rotation is only possible between -45 and +45 degrees. Accepted values: One of c("none", "random", "user").

• user_rotation (character(1))
  Only used when rotation = "user". Angle(s) (in degrees) by which the rectangular grid is to be rotated in each repetition. Either a vector of same length as repeats, or a single number that will be replicated length(repeats) times.

• offset (logical(1))
  Whether and how the rectangular grid should be shifted by an offset. Accepted values: One of c("none", "random", "user").

• user_offset (logical(1))
  Only used when offset = "user". A list (or vector) of two components specifying a shift of the rectangular grid in (possibly rotated) x and y direction. The offset values are relative values, a value of 0.5 resulting in a one-half tile shift towards the left, or upward. If this is a list, its first (second) component refers to the rotated x (y) direction, and both components must have same length as repeats (or length 1). If a vector of length 2 (or list components have length 1), the two values will be interpreted as relative shifts in (rotated) x and y direction, respectively, and will therefore be recycled as needed (length(repeats) times each).

• reassign (logical(1))
  If TRUE, 'small' tiles (as per min_frac and min_n) are merged with (smallest) adjacent tiles. If FALSE, small tiles are 'eliminated', i.e., set to NA.

• min_frac (numeric(1))
  Value must be >=0, <1. Minimum relative size of partition as percentage of sample.

• min_n (integer(1))
  Minimum number of samples per partition.

• iterate (integer(1))
  Passed down to sperrorest::tile_neighbors().

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVTiles

Active bindings

Methods

Public methods

• ResamplingRepeatedSpCVTiles$new()

• ResamplingRepeatedSpCVTiles$folds()

• ResamplingRepeatedSpCVTiles$repeats()

• ResamplingRepeatedSpCVTiles$instantiate()

• ResamplingRepeatedSpCVTiles$clone()

Method new()

Create a "Spatial 'Tiles' resampling" resampling instance.

For a list of available arguments, please see sperrorest::partition_tiles.

Usage

ResamplingRepeatedSpCVTiles$new(id = "repeated_spcv_tiles")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVTiles$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVTiles$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVTiles$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVTiles$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

See Also

ResamplingSpCVBlock

Examples

if (mlr3misc::require_namespaces("sperrorest", quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_tiles",
    repeats = 2,
    nsplit = c(4L, 3L), reassign = FALSE)
  rrcv$instantiate(task)

  # Individual sets:
  rrcv$iters
  rrcv$folds(10:12)
  rrcv$repeats(10:12)

  # Individual sets:
  rrcv$train_set(1)
  rrcv$test_set(1)
  intersect(rrcv$train_set(1), rrcv$test_set(1))

  # Internal storage:
  rrcv$instance # table
}

Description

Splits data using Leave-Location-Out (LLO), Leave-Time-Out (LTO) and Leave-Location-and-Time-Out (LLTO) partitioning. See the upstream implementation at CreateSpacetimeFolds() (package CAST) and Meyer et al. (2018) for further information.

Details

LLO predicts on unknown locations i.e. complete locations are left out in the training sets. The "space" role in Task$col_roles identifies spatial units. If stratify is TRUE, the target distribution is similar in each fold. This is useful for land cover classification when the observations are polygons. In this case, LLO with stratification should be used to hold back complete polygons and have a similar target distribution in each fold. LTO leaves out complete temporal units which are identified by the "time" role in Task$col_roles. LLTO leaves out spatial and temporal units. See the examples.

Parameters

• folds (integer(1))
  Number of folds.

• stratify
  If TRUE, stratify on the target column.

• repeats (integer(1))
  Number of repeats.

Super class

mlr3::Resampling -> ResamplingRepeatedSptCVCstf

Active bindings

Methods

Public methods

• ResamplingRepeatedSptCVCstf$new()

• ResamplingRepeatedSptCVCstf$folds()

• ResamplingRepeatedSptCVCstf$repeats()

• ResamplingRepeatedSptCVCstf$instantiate()

• ResamplingRepeatedSptCVCstf$clone()

Method new()

Create a "Spacetime Folds" resampling instance.

Usage

ResamplingRepeatedSptCVCstf$new(id = "repeated_sptcv_cstf")

Arguments

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSptCVCstf$folds(iters)

Arguments

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSptCVCstf$repeats(iters)

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSptCVCstf$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSptCVCstf$clone(deep = FALSE)

Arguments

References

Zhao Y, Karypis G (2002). “Evaluation of Hierarchical Clustering Algorithms for Document Datasets.” 11th Conference of Information and Knowledge Management (CIKM), 51-524. doi:10.1145/584792.584877.

Examples

library(mlr3)
task = tsk("cookfarm_mlr3")
task$set_col_roles("SOURCEID", roles = "space")
task$set_col_roles("Date", roles = "time")

# Instantiate Resampling
rcv = rsmp("repeated_sptcv_cstf", folds = 5, repeats = 2)
rcv$instantiate(task)

### Individual sets:
# rcv$train_set(1)
# rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
# rcv$instance # table

Description

This function creates spatially separated folds based on a distance to number of row and/or column. It assigns blocks to the training and testing folds randomly, systematically or in a checkerboard pattern. The distance (size) should be in metres, regardless of the unit of the reference system of the input data (for more information see the details section). By default, the function creates blocks according to the extent and shape of the spatial sample data (x e.g. the species occurrence), Alternatively, blocks can be created based on r assuming that the user has considered the landscape for the given species and case study. Blocks can also be offset so the origin is not at the outer corner of the rasters. Instead of providing a distance, the blocks can also be created by specifying a number of rows and/or columns and divide the study area into vertical or horizontal bins, as presented in Wenger & Olden (2012) and Bahn & McGill (2012). Finally, the blocks can be specified by a user-defined spatial polygon layer.

Details

To maintain consistency, all functions in this package use meters as their unit of measurement. However, when the input map has a geographic coordinate system (in decimal degrees), the block size is calculated by dividing the size parameter by deg_to_metre (which defaults to 111325 meters, the standard distance of one degree of latitude on the Equator). In reality, this value varies by a factor of the cosine of the latitude. So, an alternative sensible value could be cos(mean(sf::st_bbox(x)[c(2,4)]) * pi/180) * 111325.

The offset can be used to change the spatial position of the blocks. It can also be used to assess the sensitivity of analysis results to shifting in the blocking arrangements. These options are available when size is defined. By default the region is located in the middle of the blocks and by setting the offsets, the blocks will shift.

Roberts et. al. (2017) suggest that blocks should be substantially bigger than the range of spatial autocorrelation (in model residual) to obtain realistic error estimates, while a buffer with the size of the spatial autocorrelation range would result in a good estimation of error. This is because of the so-called edge effect (O'Sullivan & Unwin, 2014), whereby points located on the edges of the blocks of opposite sets are not separated spatially. Blocking with a buffering strategy overcomes this issue (see cv_buffer).

mlr3spatiotempcv notes

By default blockCV::cv_spatial() does not allow the creation of multiple repetitions. mlr3spatiotempcv adds support for this when using the size argument for fold creation. When supplying a vector of length(repeats) for argument size, these different settings will be used to create folds which differ among the repetitions.

Multiple repetitions are not possible when using the "row & cols" approach because the created folds will always be the same.

The 'Description' and 'Details' fields are inherited from the respective upstream function.

For a list of available arguments, please see blockCV::cv_spatial.

blockCV >= 3.0.0 changed the argument names of the implementation. For backward compatibility, mlr3spatiotempcv is still using the old ones. Here's a list which shows the mapping between blockCV < 3.0.0 and blockCV >= 3.0.0:

• range -> size

• rasterLayer -> r

• speciesData -> points

• showBlocks -> plot

• cols and rows -> rows_cols

The default of argument hexagon is different in mlr3spatiotempcv (FALSE instead of TRUE) to create square blocks instead of hexagonal blocks by default.

Super class

mlr3::Resampling -> ResamplingSpCVBlock

Public fields

Active bindings

Methods

Public methods

• ResamplingSpCVBlock$new()

• ResamplingSpCVBlock$instantiate()

• ResamplingSpCVBlock$clone()

Method new()

Create an "spatial block" resampling instance.

For a list of available arguments, please see blockCV::cv_spatial().

Usage

ResamplingSpCVBlock$new(id = "spcv_block")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVBlock$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVBlock$clone(deep = FALSE)

Arguments

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_block", range = 3000L, folds = 3)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
  rcv$instance
}

Description

This function generates spatially separated train and test folds by considering buffers of the specified distance (size parameter) around each observation point. This approach is a form of leave-one-out cross-validation. Each fold is generated by excluding nearby observations around each testing point within the specified distance (ideally the range of spatial autocorrelation, see cv_spatial_autocor). In this method, the testing set never directly abuts a training sample (e.g. presence or absence; 0s and 1s). For more information see the details section.

Details

When working with presence-background (presence and pseudo-absence) species distribution data (should be specified by presence_bg = TRUE argument), only presence records are used for specifying the folds (recommended). Consider a target presence point. The buffer is defined around this target point, using the specified range (size). By default, the testing fold comprises only the target presence point (all background points within the buffer are also added when add_bg = TRUE). Any non-target presence points inside the buffer are excluded. All points (presence and background) outside of buffer are used for the training set. The methods cycles through all the presence data, so the number of folds is equal to the number of presence points in the dataset.

For presence-absence data (and all other types of data), folds are created based on all records, both presences and absences. As above, a target observation (presence or absence) forms a test point, all presence and absence points other than the target point within the buffer are ignored, and the training set comprises all presences and absences outside the buffer. Apart from the folds, the number of training-presence, training-absence, testing-presence and testing-absence records is stored and returned in the records table. If column = NULL and presence_bg = FALSE, the procedure is like presence-absence data. All other data types (continuous, count or multi-class responses) should be done by presence_bg = FALSE.

mlr3spatiotempcv notes

The 'Description' and 'Details' fields are inherited from the respective upstream function. For a list of available arguments, please see blockCV::cv_buffer.

blockCV >= 3.0.0 changed the argument names of the implementation. For backward compatibility, mlr3spatiotempcv is still using the old ones. Here's a list which shows the mapping between blockCV < 3.0.0 and blockCV >= 3.0.0:

• theRange -> size

• addBG -> add_bg

• spDataType (character vector) -> presence_bg (boolean)

Super class

mlr3::Resampling -> ResamplingSpCVBuffer

Active bindings

Methods

Public methods

• ResamplingSpCVBuffer$new()

• ResamplingSpCVBuffer$instantiate()

• ResamplingSpCVBuffer$clone()

Method new()

Create an "Environmental Block" resampling instance.

For a list of available arguments, please see blockCV::cv_buffer().

Usage

ResamplingSpCVBuffer$new(id = "spcv_buffer")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVBuffer$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVBuffer$clone(deep = FALSE)

Arguments

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

See Also

ResamplingSpCVDisc

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_buffer", theRange = 10000)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
  # rcv$instance
}

Description

Splits data by clustering in the coordinate space. See the upstream implementation at sperrorest::partition_kmeans() and Brenning (2012) for further information.

Details

Universal partitioning method that splits the data in the coordinate space. Useful for spatially homogeneous datasets that cannot be split well with rectangular approaches like ResamplingSpCVBlock.

Parameters

• folds (integer(1))
  Number of folds.

Super class

mlr3::Resampling -> ResamplingSpCVCoords

Active bindings

Methods

Public methods

• ResamplingSpCVCoords$new()

• ResamplingSpCVCoords$instantiate()

• ResamplingSpCVCoords$clone()

Method new()

Create an "coordinate-based" repeated resampling instance.

For a list of available arguments, please see sperrorest::partition_cv.

Usage

ResamplingSpCVCoords$new(id = "spcv_coords")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVCoords$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVCoords$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

Examples

library(mlr3)
task = tsk("ecuador")

# Instantiate Resampling
rcv = rsmp("spcv_coords", folds = 5)
rcv$instantiate(task)

# Individual sets:
rcv$train_set(1)
rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
rcv$instance # table

Description

Spatial partitioning using circular test areas of one of more observations. Optionally, a buffer around the test area can be used to exclude observations. See the upstream implementation at sperrorest::partition_disc() and Brenning (2012) for further information.

Parameters

• folds (integer(1))
  Number of folds.

• radius (numeric(1))
  Radius of test area disc.

• buffer (integer(1))
  Radius around test area disc which is excluded from training or test set.

• prob (integer(1))
  Optional argument passed down to sample().

• replace (logical(1))
  Optional argument passed down to sample(). Sample with or without replacement.

Super class

mlr3::Resampling -> ResamplingSpCVDisc

Active bindings

Methods

Public methods

• ResamplingSpCVDisc$new()

• ResamplingSpCVDisc$instantiate()

• ResamplingSpCVDisc$clone()

Method new()

Create a "Spatial 'Disc' resampling" resampling instance.

For a list of available arguments, please see sperrorest::partition_disc.

Usage

ResamplingSpCVDisc$new(id = "spcv_disc")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVDisc$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVDisc$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

Examples

library(mlr3)
task = tsk("ecuador")

# Instantiate Resampling
rcv = rsmp("spcv_disc", folds = 3L, radius = 200L, buffer = 200L)
rcv$instantiate(task)

# Individual sets:
rcv$train_set(1)
rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
rcv$instance # table

Description

Splits data by clustering in the feature space. See the upstream implementation at blockCV::cv_cluster() and Valavi et al. (2018) for further information.

Details

Useful when the dataset is supposed to be split on environmental information which is present in features. The method allows for a combination of multiple features for clustering.

The input of raster images directly as in blockCV::cv_cluster() is not supported. See mlr3spatial and its raster DataBackends for such support in mlr3.

Parameters

• folds (integer(1))
  Number of folds.

• features (character())
  The features to use for clustering.

Super class

mlr3::Resampling -> ResamplingSpCVEnv

Active bindings

Methods

Public methods

• ResamplingSpCVEnv$new()

• ResamplingSpCVEnv$instantiate()

• ResamplingSpCVEnv$clone()

Method new()

Create an "Environmental Block" resampling instance.

For a list of available arguments, please see blockCV::cv_cluster.

Usage

ResamplingSpCVEnv$new(id = "spcv_env")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVEnv$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVEnv$clone(deep = FALSE)

Arguments

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_env", folds = 4)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
  rcv$instance
}

Description

This function implements the kNNDM algorithm and returns the necessary indices to perform a k-fold NNDM CV for map validation.

Details

knndm is a k-fold version of NNDM LOO CV for medium and large datasets. Brielfy, the algorithm tries to find a k-fold configuration such that the integral of the absolute differences (Wasserstein W statistic) between the empirical nearest neighbour distance distribution function between the test and training data during CV (Gj*), and the empirical nearest neighbour distance distribution function between the prediction and training points (Gij), is minimised. It does so by performing clustering of the training points' coordinates for different numbers of clusters that range from k to N (number of observations), merging them into k final folds, and selecting the configuration with the lowest W.

Using a projected CRS in 'knndm' has large computational advantages since fast nearest neighbour search can be done via the 'FNN' package, while working with geographic coordinates requires computing the full spherical distance matrices. As a clustering algorithm, 'kmeans' can only be used for projected CRS while 'hierarchical' can work with both projected and geographical coordinates, though it requires calculating the full distance matrix of the training points even for a projected CRS.

In order to select between clustering algorithms and number of folds 'k', different 'knndm' configurations can be run and compared, being the one with a lower W statistic the one that offers a better match. W statistics between 'knndm' runs are comparable as long as 'tpoints' and 'predpoints' or 'modeldomain' stay the same.

Map validation using 'knndm' should be used using 'CAST::global_validation', i.e. by stacking all out-of-sample predictions and evaluating them all at once. The reasons behind this are 1) The resulting folds can be unbalanced and 2) nearest neighbour functions are constructed and matched using all CV folds simultaneously.

If training data points are very clustered with respect to the prediction area and the presented 'knndm' configuration still show signs of Gj* > Gij, there are several things that can be tried. First, increase the 'maxp' parameter; this may help to control for strong clustering (at the cost of having unbalanced folds). Secondly, decrease the number of final folds 'k', which may help to have larger clusters.

The 'modeldomain' is either a sf polygon that defines the prediction area, or alternatively a SpatRaster out of which a polygon, transformed into the CRS of the training points, is defined as the outline of all non-NA cells. Then, the function takes a regular point sample (amount defined by 'samplesize') from the spatial extent. As an alternative use 'predpoints' instead of 'modeldomain', if you have already defined the prediction locations (e.g. raster pixel centroids). When using either 'modeldomain' or 'predpoints', we advise to plot the study area polygon and the training/prediction points as a previous step to ensure they are aligned.

'knndm' can also be performed in the feature space by setting 'space' to "feature". Euclidean distances or Mahalanobis distances can be used for distance calculation, but only Euclidean are tested. In this case, nearest neighbour distances are calculated in n-dimensional feature space rather than in geographical space. 'tpoints' and 'predpoints' can be data frames or sf objects containing the values of the features. Note that the names of 'tpoints' and 'predpoints' must be the same. 'predpoints' can also be missing, if 'modeldomain' is of class SpatRaster. In this case, the values of of the SpatRaster will be extracted to the 'predpoints'. In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances [Experimental]. If categorical features are present, and 'clustering' = "kmeans", K-Prototype clustering will be performed instead.

Parameters

• folds (integer(1))
  Number of folds.

• stratify
  If TRUE, stratify on the target column.

Super class

mlr3::Resampling -> ResamplingSpCVKnndm

Active bindings

Methods

Public methods

• ResamplingSpCVKnndm$new()

• ResamplingSpCVKnndm$instantiate()

• ResamplingSpCVKnndm$clone()

Method new()

Create a "K-fold Nearest Neighbour Distance Matching" resampling instance.

Usage

ResamplingSpCVKnndm$new(id = "spcv_knndm")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVKnndm$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVKnndm$clone(deep = FALSE)

Arguments

References

Linnenbrink, J., Mila, C., Ludwig, M., Meyer, H. (2023). “kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation.” EGUsphere, 2023, 1–16. doi:10.5194/egusphere-2023-1308, https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1308/.

Examples

if (mlr3misc::require_namespaces(c("sf", "CAST"), quietly = TRUE)) {
  library(mlr3)
  library(sf)

  set.seed(42)
  task = tsk("ecuador")
  points = sf::st_as_sf(task$coordinates(), crs = task$crs, coords = c("x", "y"))
  modeldomain = sf::st_as_sfc(sf::st_bbox(points))

  set.seed(42)
  cv_knndm = rsmp("spcv_knndm", modeldomain = modeldomain)
  cv_knndm$instantiate(task)

  #' ### Individual sets:
  # cv_knndm$train_set(1)
  # cv_knndm$test_set(1)
  # check that no obs are in both sets
  intersect(cv_knndm$train_set(1), cv_knndm$test_set(1)) # good!

  # Internal storage:
  # cv_knndm$instance # table
}

Description

Spatial partitioning using rectangular tiles. Small partitions can optionally be merged into adjacent ones to avoid partitions with too few observations. This method is similar to ResamplingSpCVBlock by making use of rectangular zones in the coordinate space. See the upstream implementation at sperrorest::partition_disc() and Brenning (2012) for further information.

Parameters

• dsplit (integer(2))
  Equidistance of splits in (possibly rotated) x direction (dsplit[1]) and y direction (dsplit[2]) used to define tiles. If dsplit is of length 1, its value is recycled. Either dsplit or nsplit must be specified.

• nsplit (integer(2))
  Number of splits in (possibly rotated) x direction (nsplit[1]) and y direction (nsplit[2]) used to define tiles. If nsplit is of length 1, its value is recycled.

• rotation (character(1))
  Whether and how the rectangular grid should be rotated; random rotation is only possible between -45 and +45 degrees. Accepted values: One of c("none", "random", "user").

• user_rotation (character(1))
  Only used when rotation = "user". Angle(s) (in degrees) by which the rectangular grid is to be rotated in each repetition. Either a vector of same length as repeats, or a single number that will be replicated length(repeats) times.

• offset (logical(1))
  Whether and how the rectangular grid should be shifted by an offset. Accepted values: One of c("none", "random", "user").

• user_offset (logical(1))
  Only used when offset = "user". A list (or vector) of two components specifying a shift of the rectangular grid in (possibly rotated) x and y direction. The offset values are relative values, a value of 0.5 resulting in a one-half tile shift towards the left, or upward. If this is a list, its first (second) component refers to the rotated x (y) direction, and both components must have same length as repeats (or length 1). If a vector of length 2 (or list components have length 1), the two values will be interpreted as relative shifts in (rotated) x and y direction, respectively, and will therefore be recycled as needed (length(repeats) times each).

• reassign (logical(1))
  If TRUE, 'small' tiles (as per min_frac and min_n) are merged with (smallest) adjacent tiles. If FALSE, small tiles are 'eliminated', i.e., set to NA.

• min_frac (numeric(1))
  Value must be >=0, <1. Minimum relative size of partition as percentage of sample.

• min_n (integer(1))
  Minimum number of samples per partition.

• iterate (integer(1))
  Passed down to sperrorest::tile_neighbors().

Super class

mlr3::Resampling -> ResamplingSpCVTiles

Active bindings

Methods

Public methods

• ResamplingSpCVTiles$new()

• ResamplingSpCVTiles$instantiate()

• ResamplingSpCVTiles$clone()

Method new()

Create a "Spatial 'Tiles' resampling" resampling instance.

Usage

ResamplingSpCVTiles$new(id = "spcv_tiles")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVTiles$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVTiles$clone(deep = FALSE)

Arguments

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2012.6352393.

See Also

ResamplingSpCVBlock

Examples

if (mlr3misc::require_namespaces("sperrorest", quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_tiles", nsplit = c(4L, 3L), reassign = FALSE)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  # check that no obs are in both sets
  intersect(rcv$train_set(1), rcv$test_set(1)) # good!

  # Internal storage:
  rcv$instance # table
}

Description

Splits data using Leave-Location-Out (LLO), Leave-Time-Out (LTO) and Leave-Location-and-Time-Out (LLTO) partitioning. See the upstream implementation at CreateSpacetimeFolds() (package CAST) and Meyer et al. (2018) for further information.

Details

LLO predicts on unknown locations i.e. complete locations are left out in the training sets. The "space" role in Task$col_roles identifies spatial units. If stratify is TRUE, the target distribution is similar in each fold. This is useful for land cover classification when the observations are polygons. In this case, LLO with stratification should be used to hold back complete polygons and have a similar target distribution in each fold. LTO leaves out complete temporal units which are identified by the "time" role in Task$col_roles. LLTO leaves out spatial and temporal units. See the examples.

Parameters

• folds (integer(1))
  Number of folds.

• stratify
  If TRUE, stratify on the target column.

Super class

mlr3::Resampling -> ResamplingSptCVCstf

Active bindings

Methods

Public methods

• ResamplingSptCVCstf$new()

• ResamplingSptCVCstf$instantiate()

• ResamplingSptCVCstf$clone()

Method new()

Create a "Spacetime Folds" resampling instance.

Usage

ResamplingSptCVCstf$new(id = "sptcv_cstf")

Arguments

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSptCVCstf$instantiate(task)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSptCVCstf$clone(deep = FALSE)

Arguments

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. doi:10.1016/j.envsoft.2017.12.001.

Examples

library(mlr3)
task = tsk("cookfarm_mlr3")
task$set_col_roles("SOURCEID", roles = "space")
task$set_col_roles("Date", roles = "time")

# Instantiate Resampling
rcv = rsmp("sptcv_cstf", folds = 5)
rcv$instantiate(task)

### Individual sets:
# rcv$train_set(1)
# rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
# rcv$instance # table

Description

The R.J. Cook Agronomy Farm (cookfarm) is a Long-Term Agroecosystem Research Site operated by Washington State University, located near Pullman, Washington, USA. Contains spatio-temporal (3D+T) measurements of three soil properties and a number of spatial and temporal regression covariates.

Here, only the "Profiles" dataset is used from the collection. The Date column was appended from the readings dataset. In addition coordinates were appended to the task as variables "x" and "y".

The dataset was borrowed and adapted from package GSIF which was on archived on CRAN in 2021-03.

Usage

data(cookfarm_mlr3)

Format

R6::R6Class inheriting from mlr3::TaskRegr.

Usage

mlr_tasks$get("cookfarm")
tsk("cookfarm_mlr3")

Column roles

The task has set column roles "space" and "time" for variables "Date" and "SOURCEID", respectively. These are used by certain methods during partitioning, e.g., mlr_resamplings_sptcv_cstf with variant "Leave-location-and-time-out". If only one of space or time should left out, the column roles must be adjusted by the user!

References

Gasch, C.K., Hengl, T., Gräler, B., Meyer, H., Magney, T., Brown, D.J., 2015. Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D+T: the Cook Agronomy Farm data set. Spatial Statistics, 14, pp.70–90.

Gasch, C.K., D.J. Brown, E.S. Brooks, M. Yourek, M. Poggio, D.R. Cobos, C.S. Campbell, 2016? Retroactive calibration of soil moisture sensors using a two-step, soil-specific correction. Submitted to Vadose Zone Journal.

Gasch, C.K., D.J. Brown, C.S. Campbell, D.R. Cobos, E.S. Brooks, M. Chahal, M. Poggio, 2016? A field-scale sensor network data set for monitoring and modeling the spatial and temporal variation of soil moisture in a dryland agricultural field. Submitted to Water Resources Research.

See Also

Dictionary of Tasks: mlr3::mlr_tasks

as.data.table(mlr_tasks) for a complete table of all (also dynamically created) Tasks.

Other Task: TaskClassifST, TaskRegrST, mlr_tasks_diplodia, mlr_tasks_ecuador

Description

Data set created by Patrick Schratz, University of Jena (Germany) and Eugenia Iturritxa, NEIKER, Vitoria-Gasteiz (Spain). This dataset should be cited as Schratz et al. (2019) (see reference below). The publication also contains additional information on data collection. The data set provided here shows infections of trees by the pathogen Diplodia Sapinea in the Basque Country in Spain. Predictors are environmental variables like temperature, precipitation, soil and more.

Usage

data(diplodia)

Format

R6::R6Class inheriting from mlr3::TaskClassif.

Usage

mlr_tasks$get("diplodia")
tsk("diplodia")

References

Schratz P, Muenchow J, Iturritxa E, Richter J, Brenning A (2019). “Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data.” Ecological Modelling, 406, 109–120. doi:10.1016/j.ecolmodel.2019.06.002.

See Also

Dictionary of Tasks: mlr3::mlr_tasks

as.data.table(mlr_tasks) for a complete table of all (also dynamically created) Tasks.

Other Task: TaskClassifST, TaskRegrST, mlr_tasks_cookfarm_mlr3, mlr_tasks_ecuador

Description

Data set created by Jannes Muenchow, University of Erlangen-Nuernberg, Germany. This dataset should be cited as Muenchow et al. (2012) (see reference below). The publication also contains additional information on data collection and the geomorphology of the area. The data set provided here is (a subset of) the one from the 'natural' part of the RBSF area and corresponds to landslide distribution in the year 2000.

Usage

data(ecuador)

Format

R6::R6Class inheriting from mlr3::TaskClassif.

Usage

mlr_tasks$get("ecuador")
tsk("ecuador")

References

Muenchow, J., Brenning, A., Richter, M., 2012. Geomorphic process rates of landslides along a humidity gradient in the tropical Andes. Geomorphology, 139-140: 271-284.

See Also

Dictionary of Tasks: mlr3::mlr_tasks

as.data.table(mlr_tasks) for a complete table of all (also dynamically created) Tasks.

Other Task: TaskClassifST, TaskRegrST, mlr_tasks_cookfarm_mlr3, mlr_tasks_diplodia

Description

This task specializes mlr3::Task and mlr3::TaskSupervised for spatiotemporal classification problems. The target column is assumed to be a factor. The task_type is set to "classif" and "spatiotemporal".

A spatial example task is available via tsk("ecuador"), a spatiotemporal one via tsk("cookfarm_mlr3").

The coordinate reference system passed during initialization must match the one which was used during data creation, otherwise offsets of multiple meters may occur. By default, coordinates are not used as features. This can be changed by setting coords_as_features = TRUE.

Super classes

mlr3::Task -> mlr3::TaskSupervised -> mlr3::TaskClassif -> TaskClassifST

Active bindings

Methods

Public methods

• TaskClassifST$new()

• TaskClassifST$coordinates()

• TaskClassifST$print()

• TaskClassifST$clone()

Method new()

Create a new spatiotemporal resampling Task

Usage

TaskClassifST$new(
  id,
  backend,
  target,
  positive = NULL,
  label = NA_character_,
  coordinate_names,
  crs = NA_character_,
  coords_as_features = FALSE,
  extra_args = list()
)

Arguments

Method coordinates()

Returns coordinates of observations.

Usage

TaskClassifST$coordinates(row_ids = NULL)

Arguments

Returns

data.table::data.table()

Method print()

Print the task.

Usage

TaskClassifST$print(...)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

TaskClassifST$clone(deep = FALSE)

Arguments

See Also

Other Task: TaskRegrST, mlr_tasks_cookfarm_mlr3, mlr_tasks_diplodia, mlr_tasks_ecuador

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  task = as_task_classif_st(ecuador,
    target = "slides",
    positive = "TRUE", coordinate_names = c("x", "y")
  )

  # passing objects of class 'sf' is also supported
  data_sf = sf::st_as_sf(ecuador, coords = c("x", "y"))
  task = as_task_classif_st(data_sf, target = "slides", positive = "TRUE")

  task$task_type
  task$formula()
  task$class_names
  task$positive
  task$negative
  task$coordinates()
  task$coordinate_names
}

Description

This task specializes mlr3::Task and mlr3::TaskSupervised for spatiotemporal classification problems.

A spatial example task is available via tsk("ecuador"), a spatiotemporal one via tsk("cookfarm_mlr3").

The coordinate reference system passed during initialization must match the one which was used during data creation, otherwise offsets of multiple meters may occur. By default, coordinates are not used as features. This can be changed by setting coords_as_features = TRUE.

Super classes

mlr3::Task -> mlr3::TaskSupervised -> mlr3::TaskRegr -> TaskRegrST

Active bindings

Methods

Public methods

• TaskRegrST$new()

• TaskRegrST$coordinates()

• TaskRegrST$print()

• TaskRegrST$clone()

Method new()

Create a new spatiotemporal resampling Task Returns coordinates of observations.

Usage

TaskRegrST$new(
  id,
  backend,
  target,
  label = NA_character_,
  coordinate_names,
  crs = NA_character_,
  coords_as_features = FALSE,
  extra_args = list()
)

Arguments

Method coordinates()

Usage

TaskRegrST$coordinates(row_ids = NULL)

Arguments

Returns

data.table::data.table()