ipcw.dcov.test: Performs a permutation test based on the IPCW distance...

In dcortools: Providing Fast and Flexible Functions for Distance Correlation Analysis

Performs a permutation test based on the IPCW distance covariance.

Description

Performs a permutation test based on the IPCW distance covariance.

Usage

ipcw.dcov.test(
  Y,
  X,
  affine = FALSE,
  standardize = FALSE,
  timetrafo = "none",
  type.X = "sample",
  metr.X = "euclidean",
  use = "all",
  cutoff = NULL,
  B = 499
)

Arguments

Y	A column with two rows, where the first row contains the survival times and the second row the status indicators (a survival object will work).
X	A vector or matrix containing the covariate information.
affine	logical; indicates if X should be transformed such that the result is invariant under affine transformations of X.
standardize	logical; should X be standardized using the standard deviations of single observations. No effect when affine = TRUE.
timetrafo	specifies a transformation applied on the follow-up times. Can be "none", "log" or a user-specified function.
type.X	For "distance", X is interpreted as a distance matrix. For "sample" (or any other value), X is interpreted as a sample.
metr.X	metr.X specifies the metric which should be used for X to analyze the distance covariance. Options are "euclidean", "discrete", "alpha", "minkowski", "gaussian", "gaussauto" and "boundsq". For "alpha", "minkowski", "gauss", "gaussauto" and "boundsq", the corresponding parameters are specified via "c(metric,parameter)" (see examples); the standard parameter is 2 for "minkowski" and "1" for all other metrics.
use	specifies how to treat missing values. "complete.obs" excludes observations containing NAs, "all" uses all observations.
cutoff	If provided, all survival times larger than cutoff are set to the cutoff and all corresponding status indicators are set to one. Under most circumstances, choosing a cutoff is highly recommended.
B	The number of permutations used for the permutation test

Value

An list with two arguments, $dcov contains the IPCW distance covariance, $pvalue the corresponding p-value

References

\insertRef

bottcher2017detectingdcortools

\insertRef

datta2010inversedcortools

\insertRef

dueck2014affinelydcortools

\insertRef

huo2016fastdcortools

\insertRef

lyons2013distancedcortools

\insertRef

sejdinovic2013equivalencedcortools

\insertRef

szekely2007dcortools

\insertRef

szekely2009browniandcortools

Examples

X <- rnorm(100)
survtime <- rgamma(100, abs(X))
cens <- rexp(100)
status <- as.numeric(survtime < cens)
time <- sapply(1:100, function(u) min(survtime[u], cens[u]))
surv <- cbind(time, status)
ipcw.dcov.test(surv, X)
ipcw.dcov.test(surv, X, cutoff = quantile(time, 0.8)) 
# often better performance when using a cutoff time

dcortools documentation built on Dec. 10, 2022, 1:09 a.m.