R: Estimation of scale based on sequential-order differences

colIQRDiffs {DelayedMatrixStats}

R Documentation

Estimation of scale based on sequential-order differences

Description

Estimation of scale based on sequential-order differences, corresponding to the scale estimates provided by var, sd, mad and IQR.

Usage

colIQRDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

colMadDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

colSdDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

colVarDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

rowIQRDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

rowMadDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

rowSdDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

rowVarDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  ...
)

## S4 method for signature 'DelayedMatrix'
colIQRDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
colMadDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
colSdDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
colVarDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
rowIQRDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
rowMadDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
rowSdDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
rowVarDiffs(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  diff = 1L,
  trim = 0,
  force_block_processing = FALSE,
  ...
)

Arguments

`x`	A NxK DelayedMatrix.
`rows`	A `vector` indicating subset of elements (or rows and/or columns) to operate over. If `NULL`, no subsetting is done.
`cols`	A `vector` indicating subset of elements (or rows and/or columns) to operate over. If `NULL`, no subsetting is done.
`na.rm`	If `TRUE`, `NA`s are excluded, otherwise not.
`diff`	The positional distance of elements for which the difference should be calculated.
`trim`	A `double` in [0,1/2] specifying the fraction of observations to be trimmed from each end of (sorted) `x` before estimation.
`...`	Additional arguments passed to specific methods.
`force_block_processing`	`FALSE` (the default) means that a seed-aware, optimised method is used (if available). This can be overridden to use the general block-processing strategy by setting this to `TRUE` (typically not advised). The block-processing strategy loads one or more (depending on `\link[DelayedArray]{getAutoBlockSize}()`) columns (`colFoo()`) or rows (`rowFoo()`) into memory as an ordinary base::array.

Details

Note that n-order difference MAD estimates, just like the ordinary MAD estimate by mad, apply a correction factor such that the estimates are consistent with the standard deviation under Gaussian distributions.

The interquartile range (IQR) estimates does not apply such a correction factor. If asymptotically normal consistency is wanted, the correction factor for IQR estimate is 1 / (2 * qnorm(3/4)), which is half of that used for MAD estimates, which is 1 / qnorm(3/4). This correction factor needs to be applied manually, i.e. there is no constant argument for the IQR functions.

Value

Returns a numeric vector of length 1, length N, or length K.

Author(s)

Peter Hickey

References

[1] J. von Neumann et al., The mean square successive difference. Annals of Mathematical Statistics, 1941, 12, 153-162.

Examples

# A DelayedMatrix with a 'Matrix' seed
dm_Matrix <- DelayedArray(Matrix::Matrix(c(rep(1L, 5),
                                           as.integer((0:4) ^ 2),
                                           seq(-5L, -1L, 1L)),
                                         ncol = 3))
# A DelayedMatrix with a 'SolidRleArraySeed' seed
dm_Rle <- RleArray(Rle(c(rep(1L, 5),
                         as.integer((0:4) ^ 2),
                         seq(-5L, -1L, 1L))),
                   dim = c(5, 3))

colIQRDiffs(dm_Matrix)

colMadDiffs(dm_Matrix)

colSdDiffs(dm_Matrix)

colVarDiffs(dm_Matrix)

# Only using rows 2-4
rowIQRDiffs(dm_Rle, rows = 2:4)

# Only using rows 2-4
rowMadDiffs(dm_Rle, rows = 2:4)

# Only using rows 2-4
rowSdDiffs(dm_Rle, rows = 2:4)

# Only using rows 2-4
rowVarDiffs(dm_Rle, rows = 2:4)

[Package DelayedMatrixStats version 1.10.1 Index]