cum.residuals: Model validation based on cumulative residuals

In timereg: Flexible Regression Models for Survival Data

Model validation based on cumulative residuals

Description

Computes cumulative residuals and approximative p-values based on resampling techniques.

Usage

cum.residuals(
  object,
  data = parent.frame(),
  modelmatrix = 0,
  cum.resid = 1,
  n.sim = 500,
  weighted.test = 0,
  max.point.func = 50,
  weights = NULL
)

Arguments

object	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.
cum.resid	to compute residuals versus each of the continuous covariates in the model.
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.
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)

timereg documentation built on Sept. 11, 2024, 8:35 p.m.