gofM.phreg {mets} | R Documentation |
GOF for Cox covariates in PH regression
Description
Cumulative residuals after model matrix for Cox PH regression p-values based on Lin, Wei, Ying resampling.
Usage
gofM.phreg(
formula,
data,
offset = NULL,
weights = NULL,
modelmatrix = NULL,
n.sim = 1000,
silent = 1,
...
)
Arguments
formula |
formula for cox regression |
data |
data for model |
offset |
offset |
weights |
weights |
modelmatrix |
matrix for cumulating residuals |
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
That is, computes
U(t) = \int_0^t M^t d \hat M
and resamples its asymptotic distribution.
This will show if the residuals are consistent with the model. Typically, M will be a design matrix for the continous covariates that gives for example the quartiles, and then the plot will show if for the different quartiles of the covariate the risk prediction is consistent over time (time x covariate interaction).
Author(s)
Thomas Scheike and Klaus K. Holst
Examples
library(mets)
data(TRACE)
set.seed(1)
TRACEsam <- blocksample(TRACE,idvar="id",replace=FALSE,100)
dcut(TRACEsam) <- ~.
mm <- model.matrix(~-1+factor(wmicat.4),data=TRACEsam)
m1 <- gofM.phreg(Surv(time,status==9)~vf+chf+wmi,data=TRACEsam,modelmatrix=mm)
summary(m1)
if (interactive()) {
par(mfrow=c(2,2))
plot(m1)
}
m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam,modelmatrix=mm)
summary(m1)
## cumulative sums in covariates, via design matrix mm
mm <- cumContr(TRACEsam$wmi,breaks=10,equi=TRUE)
m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam,
modelmatrix=mm,silent=0)
summary(m1)