Cumulative score process residuals for Cox PH regression p-values based on Lin, Wei, Ying resampling.
# S3 method for phreg
gof(object, n.sim = 1000, silent = 1, robust = NULL, ...)Arguments
- object
is phreg object
- n.sim
number of simulations for score processes
- silent
to show timing estimate will be produced for longer jobs
- robust
to control wether robust dM_i(t) or dN_i are used for simulations
- ...
Additional arguments to lower level funtions
Examples
library(mets)
data(sTRACE)
m1 <- phreg(Surv(time,status==9)~vf+chf+diabetes,data=sTRACE)
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#> Sup|U(t)| pval
#> vf 7.276731 0.014
#> chf 8.971263 0.075
#> diabetes 3.044404 0.796
par(mfrow=c(1,3))
plot(gg)
m1 <- phreg(Surv(time,status==9)~strata(vf)+chf+diabetes,data=sTRACE)
## to get Martingale ~ dN based simulations
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#> Sup|U(t)| pval
#> chf 8.036132 0.149
#> diabetes 3.441389 0.665
## to get Martingale robust simulations, specify cluster in call
sTRACE$id <- 1:500
m1 <- phreg(Surv(time,status==9)~vf+chf+diabetes+cluster(id),data=sTRACE)
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#> Sup|U(t)| pval
#> vf 7.276731 0.004
#> chf 8.971263 0.074
#> diabetes 3.044404 0.806
m1 <- phreg(Surv(time,status==9)~strata(vf)+chf+diabetes+cluster(id),data=sTRACE)
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#> Sup|U(t)| pval
#> chf 8.036132 0.147
#> diabetes 3.441389 0.664