gofZ.phreg {mets}

GOF for Cox covariates in PH regression

Description

That is, computes

U(z,\tau) = \int_0^\tau M(z)^t d \hat M

and resamples its asymptotic distribution.

Usage

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 \leq 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

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")