dcortools (version 0.1.7)

ipcw.dcor: Calculates an inverse-probability-of-censoring weighted (IPCW) distance correlation based on IPCW U-statistics datta2010inversedcortools.

Description

Calculates an inverse-probability-of-censoring weighted (IPCW) distance correlation based on IPCW U-statistics datta2010inversedcortools.

Usage

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

Value

An inverse-probability of censoring weighted estimate for the distance correlation between X and the survival times.

Arguments

Y

A matrix with two columns, where the first column contains the survival times and the second column the status indicators (a survival object will work).

X

A vector or matrix containing the covariate information.

affine

logical; specifies 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", X is interpreted as a sample.

metr.X

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)", c("gaussian",3) for example uses a Gaussian metric with bandwidth parameter 3; the default 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.

References

bottcher2017detectingdcortools

datta2010inversedcortools

dueck2014affinelydcortools

huo2016fastdcortools

lyons2013distancedcortools

sejdinovic2013equivalencedcortools

szekely2007dcortools

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.dcor(surv, X)