R: Boostrapping algorithm for 'coxed'

bootcov2 {coxed}

R Documentation

Boostrapping algorithm for `coxed`

Description

This function uses bootstrapping to create standard errors and confidence intervals for the quantities produced by the coxed() function. It is adapted from the bootcov function in the rms package. It is called by the coxed function and is not intended to be used by itself. Please refer to the original bootcov function for general bootstrapping applications.

Usage

bootcov2(fit, cluster, B = 200, fitter, coef.reps = TRUE,
  loglik = FALSE, pr = FALSE, maxit = 15, group = NULL,
  stat = NULL)

Arguments

`fit`	an estimated Cox proportional hazards model object with class "coxph" or "cph"
`cluster`	a variable indicating groupings. `cluster` may be any type of vector (factor, character, integer). Unique values of `cluster` indicate possibly correlated groupings of observations. Note the data used in the fit and stored in `fit$x` and `fit$y` may have had observations containing missing values deleted. It is assumed that if there were any `NA`s, an `naresid` function exists for the class of fit. This function restores `NA`s so that the rows of the design matrix coincide with `cluster`
`B`	Number of bootstrap simulation iterations
`fitter`	the name of a function with arguments `(x,y)` that will fit bootstrap samples. Default is taken from the class of fit if it is `ols`, `lrm`, `cph`, `psm`, `Rq`. If `fitter="tvc"` the function employs `agreg.fit`
`coef.reps`	set to `TRUE` if you want to store a matrix of all bootstrap regression coefficient estimates in the returned component `boot.Coef`.
`loglik`	set to `TRUE` to store -2 log likelihoods for each bootstrap model, evaluated against the original `x` and `y` data. The default is to do this when `coef.reps` is specified as `TRUE`. The use of `loglik=TRUE` assumes that an `oos.loglik` method exists for the type of model being analyzed, to calculate out-of-sample -2 log likelihoods (see `rmsMisc`). After the B -2 log likelihoods (stored in the element named `boot.loglik` in the returned fit object), the `B+1` element is the -2 log likelihood for the original model fit
`pr`	set to `TRUE` to print the current sample number to monitor progress
`maxit`	maximum number of iterations, to pass to `fitter`
`group`	a grouping variable used to stratify the sample upon bootstrapping. This allows one to handle `k`-sample problems, i.e., each bootstrap sample will be forced to select the same number of observations from each level of group as the number appearing in the original dataset. You may specify both `group` and `cluster`
`stat`	a single character string specifying the name of a `stats` element produced by the fitting function to save over the bootstrap repetitions. The vector of saved statistics will be in the `boot.stats` part of the list returned by `bootcov`

Details

This function contains the same code as the bootcov function in the rms package, with a few alterations to work better with the coxed function. First, we output a result attribute b.ind, which contains the observation numbers from the estimation sample that are drawn with replacement to produce the bootstrap sample and takes into account clustering. Second, we program a new class, tvc, for fitter to use agreg.fit instead of coxph.fit when the data contain time-varying covariates.

Author(s)

Jonathan Kropko <jkropko@virginia.edu> and Jeffrey J. Harden <jharden@nd.edu>, based on the code for the bootcov function in the rms package by Frank Harrell and Bill Pikounis