colIQRDiffs {DelayedMatrixStats} | R Documentation |
Estimation of scale based on sequential-order differences, corresponding to
the scale estimates provided by var
,
sd
, mad
and
IQR
.
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, ...)
x |
A NxK DelayedMatrix. |
rows |
A |
cols |
A |
na.rm |
|
diff |
The positional distance of elements for which the difference should be calculated. |
trim |
A |
... |
Additional arguments passed to specific methods. |
force_block_processing |
|
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.
Returns a numeric
vector
of
length 1, length N, or length K.
[1] J. von Neumann et al., The mean square successive
difference. Annals of Mathematical Statistics, 1941, 12, 153-162.
For the corresponding non-differentiated estimates, see
var
, sd
, mad
and IQR
. Internally, diff2
() is used
which is a faster version of diff
().
# 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)