YPmodel.lackfittest: Martingale residual-based & contrast-based lack-of-fit tests

Description

Four functions to perform the two lack-of-fit tests (martingale residual-based & contrast-based), to print results, and to draw randomly selected realizations of the process.

Usage

## S3 method for class 'lackfittest':
YPmodel(data, repNum, Internal, ...)
## S3 method for class 'YPmodel.lackfittest':
summary(object, ...)
## S3 method for class 'YPmodel.martint':
plot(x, Internal, ...)
## S3 method for class 'YPmodel.survf':
plot(x, Internal, ...)

Arguments

...

For S4 method only.

data

A properly qualified filename where text data is to be saved, or a dataframe of input data set with three vectors: the event / censoring time (unite: year), the censoring indicator, and the group membership indicator. See the structure of sample data set

repNum

Number of times the resampling simulations are done.

Internal

A dataframe of internal parameters, used only to perform hypothesis tests and plot (and to accelerate the speed).

A dataframe of the two lack-of-fit tests, generated by YPmodel.lackfittest.

object

A dataframe of the two lack-of-fit tests, generated by YPmodel.lackfittest, equally to x (different symbol for S4 method only).

Value

newBestValue of $\hat{\beta}$ used in the two tests.
pvalu1p-value from martingale residual-based test.
pvalu2p-value from contrast-based test.
mobs1Parameter used in plotting martingale residual-based test.
mobs2Parameter used inplotting contrast-based test.
obsParameter used in plotting martingale residual-based test.
obs2Parameter used in plotting contrast-based test.
wtildCount1Parameter used in plotting martingale residual-based test.
lineCount1Parameter used in plotting martingale residual-based test.
wtildCount2Parameter used in plotting contrast-based test.
lineCount2Parameter used in plotting contrast-based test.

References

YANG, S. AND ZHAO, Y. (2012). Checking the Short-Term and Long-Term Hazard Ratio Model for Survival Data. Scandinavian Journal of Statistics.

Examples

Run this code

library(YPmodel)
    data(gastric)
    LackFitTest <- YPmodel.lackfittest(data=gastric, repNum=100)
    summary.YPmodel.lackfittest(LackFitTest)
    plot(LackFitTest)

    plot.YPmodel.martint(LackFitTest)
    plot.YPmodel.survf(LackFitTest)