partition_tiles: Partition the study area into rectangular tiles
In sperrorest: Perform Spatial Error Estimation and Variable Importance Assessment

partition_tiles

R Documentation

Partition the study area into rectangular tiles

Description

partition_tiles divides the study area into a specified number of rectangular tiles. Optionally small partitions can be merged with adjacent tiles to achieve a minimum number or percentage of samples in each tile.

Usage

partition_tiles(
  data,
  coords = c("x", "y"),
  dsplit = NULL,
  nsplit = NULL,
  rotation = c("none", "random", "user"),
  user_rotation,
  offset = c("none", "random", "user"),
  user_offset,
  reassign = TRUE,
  min_frac = 0.025,
  min_n = 5,
  iterate = 1,
  return_factor = FALSE,
  repetition = 1,
  seed1 = NULL
)

Arguments

`data`	`data.frame` containing at least the columns specified by `coords`
`coords`	vector of length 2 defining the variables in `data` that contain the x and y coordinates of sample locations
`dsplit`	optional vector of length 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`	optional vector of length 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`	indicates whether and how the rectangular grid should be rotated; random rotation is only between `-45` and `+45` degrees.
`user_rotation`	if `rotation='user'`, angles (in degrees) by which the rectangular grid is to be rotated in each repetition. Either a vector of same length as `repetition`, or a single number that will be replicated `length(repetition)` times.
`offset`	indicates whether and how the rectangular grid should be shifted by an offset.
`user_offset`	if `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 `repetition` (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(repetition)` times each).
`reassign`	logical (default `TRUE`): if `TRUE`, 'small' tiles (as per `min_frac` and `min_n` arguments and get_small_tiles) are merged with (smallest) adjacent tiles. If `FALSE`, small tiles are 'eliminated', i.e. set to `NA`.
`min_frac`	numeric >=0, <1: minimum relative size of partition as percentage of sample; argument passed to get_small_tiles. Will be ignored if `NULL`.
`min_n`	integer >=0: minimum number of samples per partition; argument passed to get_small_tiles. Will be ignored if `NULL`.
`iterate`	argument to be passed to tile_neighbors
`return_factor`	if `FALSE` (default), return a represampling object; if `TRUE` (used internally by other sperrorest functions), return a `list` containing factor vectors (see Value)
`repetition`	numeric vector: cross-validation repetitions to be generated. Note that this is not the number of repetitions, but the indices of these repetitions. E.g., use `repetition = c(1:100)` to obtain (the 'first') 100 repetitions, and `repetition = c(101:200)` to obtain a different set of 100 repetitions.
`seed1`	`seed1+i` is the random seed that will be used by set.seed in repetition `i` (`i` in `repetition`) to initialize the random number generator before sampling from the data set.

Value

A represampling object. Contains length(repetition) resampling objects as repetitions. The exact number of folds / test-set tiles within each resampling objects depends on the spatial configuration of the data set and possible cleaning steps (see min_frac, min_n).

Note

Default parameter settings may change in future releases. This function, especially the rotation and shifting part of it and the algorithm for cleaning up small tiles is still a bit experimental. Use with caution. For non-zero offsets (offset!='none')), the number of tiles may actually be greater than nsplit[1]*nsplit[2] because of fractional tiles lurking into the study region. reassign=TRUE with suitable thresholds is therefore recommended for non-zero (including random) offsets.

Examples

data(ecuador)
set.seed(42)
parti <- partition_tiles(ecuador, nsplit = c(4, 3), reassign = FALSE)
# plot(parti,ecuador)
# tile A4 has only 55 samples
# same partitioning, but now merge tiles with less than 100 samples to
# adjacent tiles:
parti2 <- partition_tiles(ecuador,
  nsplit = c(4, 3), reassign = TRUE,
  min_n = 100
)
# plot(parti2,ecuador)
summary(parti2)
# tile B4 (in 'parti') was smaller than A3, therefore A4 was merged with B4,
# not with A3
# now with random rotation and offset, and tiles of 2000 m length:
parti3 <- partition_tiles(ecuador,
  dsplit = 2000, offset = "random",
  rotation = "random", reassign = TRUE, min_n = 100
)
# plot(parti3, ecuador)
summary(parti3)

sperrorest documentation built on Oct. 16, 2022, 5:05 p.m.