Calculates the distance standard deviation edelmann2017distancedcortools.
distsd(
X,
affine = FALSE,
standardize = FALSE,
bias.corr = FALSE,
type.X = "sample",
metr.X = "euclidean",
use = "all",
algorithm = "auto"
)numeric; the distance standard deviation of X.
contains either the sample or its corresponding distance matrix.
In the first case, X can be provided either as a vector (if one-dimensional), a matrix or a data.frame (if two-dimensional or higher).
In the second case, the input must be a distance matrix corresponding to the sample of interest.
If X is a sample, type.X must be specified as "sample". If X is a distance matrix, type.X must be specified as "distance".
logical; specifies if the affinely invariant distance standard deviation dueck2014affinelydcortools should be calculated or not.
logical; specifies if X and Y should be standardized dividing each component by its standard deviations. No effect when affine = TRUE.
logical; specifies if the bias corrected version of the sample distance standard deviation huo2016fastdcortools should be calculated.
For "distance", X is interpreted as a distance matrix. For "sample", X is interpreted as a sample.
specifies the metric which should be used to compute the distance matrix for X (ignored when type.X = "distance").
Options are "euclidean", "discrete", "alpha", "minkowski", "gaussian", "gaussauto", "boundsq" or user-specified metrics (see examples).
For "alpha", "minkowski", "gaussian", "gaussauto" and "boundsq", the corresponding parameters are specified via "c(metric, parameter)", e.g. c("gaussian", 3) for a Gaussian metric with bandwidth parameter 3; the default parameter is 2 for "minkowski" and "1" for all other metrics.
See lyons2013distance,sejdinovic2013equivalence,bottcher2017detecting;textualdcortools for details.
specifies how to treat missing values. "complete.obs" excludes observations containing NAs, "all" uses all observations.
specifies the algorithm used for calculating the distance standard deviation.
"fast" uses an O(n log n) algorithm if the observations are one-dimensional and metr.X and metr.Y are either "euclidean" or "discrete", see also huo2016fast;textualdcortools.
"memsave" uses a memory saving version of the standard algorithm with computational complexity O(n^2) but requiring only O(n) memory.
"standard" uses the classical algorithm. User-specified metrics always use the classical algorithm.
"auto" chooses the best algorithm for the specific setting using a rule of thumb.
bottcher2017detectingdcortools
dueck2014affinelydcortools
edelmann2017distancedcortools
huo2016fastdcortools
lyons2013distancedcortools
sejdinovic2013equivalencedcortools
szekely2007dcortools
szekely2009browniandcortools
X <- rnorm(100)
distsd(X) # for more examples on the options see the documentation of distcov.
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