pvimp.pcoxtime: Permutation variable importance

In pcoxtime: Penalized Cox Proportional Hazard Model for Time-Dependent Covariates

Permutation variable importance

Description

Computes the relative importance based on random permutation of focal variable for pcoxtime model.

Usage

pvimp.pcoxtime(
  object,
  newdata,
  nrep = 50,
  parallelize = FALSE,
  nclusters = 1,
  estimate = c("mean", "quantile"),
  probs = c(0.025, 0.5, 0.975),
  seed = NULL,
  ...
)

Arguments

object	fitted pcoxtime.
newdata	data frame containing the variables appearing on the right hand side of pcoxtime formula.
nrep	number of replicates for permutations. Default is nrep = 50.
parallelize	whether to run in parallel. Default is FALSE.
nclusters	number of cores to use if parallelize = TRUE.
estimate	character string specify which summary statistic to use for the estimates. Default is "mean".
probs	numeric vector of probabilities with values in [0,1].
seed	a single value for for random number generation.
...	for future implementation.

Details

Given predictors x_1, x_2, ..., x_n used to predict the survival outcome, y. Suppose, for example, x_1 has low predictive power for the response. Then, if we randomly permute the observed values for x_1, then the prediction for y will not change much. Conversely, if any of the predictors highly predicts the response, the permutation of that specific predictor will lead to a considerable change in the predictive measure of the model. In this case, we conclude that this predictor is important. In our implementation, Harrel's concordance index is used to measure the prediction accuracy.

Value

a named vector of variable scores (estimate = "mean") or a data frame (estimate = "quantile").

pcoxtime documentation built on May 13, 2022, 1:05 a.m.