# gofZ.phreg: GOF for Cox covariates in PH regression In mets: Analysis of Multivariate Event Times

## Description

That is, computes

U(z,τ) = \int_0^τ M(z)^t d \hat M

and resamples its asymptotic distribution.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 gofZ.phreg( formula, data, vars = NULL, offset = NULL, weights = NULL, breaks = 50, equi = FALSE, n.sim = 1000, silent = 1, ... ) 

## Arguments

 formula formula for cox regression data data for model vars which variables to test for linearity offset offset weights weights breaks number of breaks for cumulatives in covarirate direction equi equidistant breaks or not n.sim number of simulations for score processes silent to keep it absolutely silent, otherwise timing estimate will be prduced for longer jobs. ... Additional arguments to lower level funtions

## Details

This will show if the residuals are consistent with the model evaulated in the z covariate. M is here chosen based on a grid (z_1, ..., z_m) and the different columns are I(Z_i ≤q z_l). for l=1,...,m. The process in z is resampled to find extreme values. The time-points of evuluation is by default 50 points, chosen as 2

The p-value is valid but depends on the chosen grid. When the number of break points are high this will give the orginal test of Lin, Wei and Ying for linearity, that is also computed in the timereg package.

## Author(s)

Thomas Scheike and Klaus K. Holst

## Examples

  1 2 3 4 5 6 7 8 9 10 library(mets) data(TRACE) set.seed(1) TRACEsam <- blocksample(TRACE,idvar="id",replace=FALSE,100) ## cumulative sums in covariates, via design matrix mm ## Reduce Ex.Timings m1 <- gofZ.phreg(Surv(time,status==9)~strata(vf)+chf+wmi+age,data=TRACEsam) summary(m1) plot(m1,type="z") 

mets documentation built on Oct. 23, 2020, 5:55 p.m.