R: Model validation based on cumulative residuals

cum.residuals {timereg} | R Documentation

## Model validation based on cumulative residuals

### Description

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

### Usage

    cum.residuals(object, data = sys.parent(), 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)

[Package timereg version 1.9.4 Index]