ipcw.dcov.test | R Documentation |

Performs a permutation test based on the IPCW distance covariance.

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

`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 |

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

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.dcov.test(surv, X) ipcw.dcov.test(surv, X, cutoff = quantile(time, 0.8)) # often better performance when using a cutoff time

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