an object of class 'aalen', 'timecox', 'cox.aalen' where
the residuals are returned ('residuals=1')
data
data frame based on which residuals are computed.
modelmatrix
specifies a grouping of the data that
is used for cumulating residuals. Must have same size as data and be ordered in the same way.
n.sim
number of simulations in resampling.
weighted.test
to compute a variance weighted version of the
test-processes used for testing constant effects of covariates.
cum.resid
to compute residuals versus each of
the continuous covariates in the model.
max.point.func
limits the amount of computations, only considers a max of 50 points
on the covariate scales.
weights
weights for sum of martingale residuals, now for cum.resid=1.
Value
returns an object of type "cum.residuals" with the following arguments:
cum
cumulative residuals versus time for the groups specified by
modelmatrix.
var.cum
the martingale based pointwise variance estimates.
robvar.cum
robust pointwise variances estimates of cumulatives.
obs.testBeq0
observed absolute value of supremum of
cumulative components scaled with the variance.
pval.testBeq0
p-value covariate effects based on supremum test.
sim.testBeq0
resampled supremum value.
conf.band
resampling based constant to construct robust 95%
uniform confidence bands for cumulative residuals.
obs.test
absolute value of supremum of observed test-process.
pval.test
p-value for supremum test statistic.
sim.test
resampled absolute value of supremum cumulative residuals.
proc.cumz
observed cumulative residuals versus all continuous
covariates of model.
sim.test.proccumz
list of 50 random realizations of
test-processes under model for all continuous covariates.
Author(s)
Thomas Scheike
References
Martinussen and Scheike, Dynamic Regression Models for Survival Data,
Springer (2006).
Examples
data(sTRACE)
# Fits Aalen model and returns residuals
fit<-aalen(Surv(time,status==9)~age+sex+diabetes+chf+vf,
data=sTRACE,max.time=7,n.sim=0,residuals=1)
# constructs and simulates cumulative residuals versus age groups
fit.mg<-cum.residuals(fit,data=sTRACE,n.sim=100,
modelmatrix=model.matrix(~-1+factor(cut(age,4)),sTRACE))
par(mfrow=c(1,4))
# cumulative residuals with confidence intervals
plot(fit.mg);
# cumulative residuals versus processes under model
plot(fit.mg,score=1);
summary(fit.mg)
# cumulative residuals vs. covariates Lin, Wei, Ying style
fit.mg<-cum.residuals(fit,data=sTRACE,cum.resid=1,n.sim=100)
par(mfrow=c(2,4))
plot(fit.mg,score=2)
summary(fit.mg)