ipcw.dcor | R Documentation |

Calculates an inverse-probability-of-censoring weighted (IPCW) distance correlation based on IPCW U-statistics \insertCitedatta2010inversedcortools.

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

`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. |

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

bottcher2017detectingdcortools

\insertRefdatta2010inversedcortools

\insertRefdueck2014affinelydcortools

\insertRefhuo2016fastdcortools

\insertReflyons2013distancedcortools

\insertRefsejdinovic2013equivalencedcortools

\insertRefszekely2007dcortools

\insertRefszekely2009browniandcortools

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)

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