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Random or Restricted Permutation of Zoo Time Series

Source: R/zoo_permute.R
zoo_permute.Rd

Fast permutation of zoo time series for null model testing using a fast and efficient C++ implementations of different restricted and free permutation methods.

The available permutation methods are:

  • "free" (see permute_free_cpp()): Unrestricted and independent re-shuffling of individual cases across rows and columns. Individual values are relocated to a new row and column within the dimensions of the original matrix.

  • "free_by_row" (see permute_free_by_row_cpp()): Unrestricted re-shuffling of complete rows. Each individual row is given a new random row number, and the data matrix is re-ordered accordingly.

  • "restricted" (see permute_restricted_cpp()): Data re-shuffling across rows and columns is restricted to blocks of contiguous rows. The algorithm divides the data matrix into a set of blocks of contiguous rows, and individual cases are then assigned to a new row and column within their original block.

  • "restricted_by_row" (see permute_restricted_by_row_cpp()): Re-shuffling of complete rows is restricted to blocks of contiguous rows. The algorithm divides the data matrix into a set of blocks of contiguous rows, each individual row is given a new random row number within its original block, and the block is reordered accordingly to generate the permuted output.

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

zoo_permute(
  x = NULL,
  repetitions = 1L,
  permutation = "restricted_by_row",
  block_size = NULL,
  seed = 1L
)

Arguments

x

(required, zoo object) zoo time series. Default: NULL

repetitions

(optional, integer) number of permutations to compute. Large numbers may compromise your R session. Default: 1

permutation

(optional, character string) permutation method. Valid values are listed below from higher to lower induced randomness:

  • "free": unrestricted re-shuffling of individual cases across rows and columns. Ignores block_size.

  • "free_by_row": unrestricted re-shuffling of complete rows. Ignores block size.

  • "restricted": restricted shuffling across rows and columns within blocks of rows.

  • "restricted_by_row": restricted re-shuffling of rows within blocks.

block_size

(optional, integer) Block size in number of rows for restricted permutations. Only relevant when permutation methods are "restricted" or "restricted_by_row". A block of size n indicates that the original data is pre-divided into blocks of such size, and a given row can only be permuted within their original block. If NULL, defaults to the rounded one tenth of the number of rows in x. Default: NULL.

seed

(optional, integer) initial random seed to use during permutations. Default: 1

Value

Time Series List

See also

Other zoo_functions: zoo_aggregate(), zoo_name_clean(), zoo_name_get(), zoo_name_set(), zoo_plot(), zoo_resample(), zoo_smooth_exponential(), zoo_smooth_window(), zoo_time(), zoo_to_tsl(), zoo_vector_to_matrix()

Examples


#simulate zoo time series
x <- zoo_simulate(cols = 2)

if(interactive()){
  zoo_plot(x)
}

#free
x_free <- zoo_permute(
  x = x,
  permutation = "free",
  repetitions = 2
)
#> distantia::utils_block_size(): argument 'block_size' set to 10.

if(interactive()){
  tsl_plot(
    tsl = x_free,
    guide = FALSE
    )
}

#free by row
x_free_by_row <- zoo_permute(
  x = x,
  permutation = "free_by_row",
  repetitions = 2
)
#> distantia::utils_block_size(): argument 'block_size' set to 10.

if(interactive()){
  tsl_plot(
    tsl = x_free_by_row,
    guide = FALSE
  )
}

#restricted
x_restricted <- zoo_permute(
  x = x,
  permutation = "restricted",
  repetitions = 2
)
#> distantia::utils_block_size(): argument 'block_size' set to 10.

if(interactive()){
  tsl_plot(
    tsl = x_restricted,
    guide = FALSE
  )
}

#restricted by row
x_restricted_by_row <- zoo_permute(
  x = x,
  permutation = "restricted_by_row",
  repetitions = 2
)
#> distantia::utils_block_size(): argument 'block_size' set to 10.

if(interactive()){
  tsl_plot(
    tsl = x_restricted_by_row,
    guide = FALSE
  )
}