plot.scproc: Graphical model diagnostic

In goftte: Goodness-of-Fit for Time-to-Event Data

Description

Produce a graphical model diagnostic, with the possibility of adding the prediction bands calculated by predband.scproc.

Usage

## S3 method for class 'scproc'
plot(x, idx=1:length(x$variable), col=c("grey"), ci=FALSE,
                      col.ci="darkblue", col.alpha=0.3, lty.ci=0, level=0.95,
                      legend=c("type1","type2","none"), xlab=NULL, ylab=NULL,
                      ylim=NULL, xlim=NULL, title=NULL, cex.lab=1,cex.main=1,...)

Arguments

x	An object of class scproc.
idx	The index number of covariates whose the plot is required.
col	Boolean argument controlling for the plot of prediction bands. Values are TRUE or FALSE.
ci	Color of the processes realizations.
col.ci	Color of the prediction bands.
col.alpha	Color intensity of the prediction bands.
lty.ci	Line thickness of the prediction bands border.
level	The required level for prediction bands.
legend	Argument controling the type of legend on the plot. "type1" for p-values, "type2" for usual type of legends and "none" for no legend.
xlab	Label of the x-axis.
ylab	Label of the y-axis.
xlim	Vector of two components which indicates the extremum values of x-axis.
ylim	Vector of two components which indicates the extremum values of y-axis.
title	Vector of length idx whose components are titles of plots.
cex.lab	Magnification of x and y labels relative to cex.
cex.main	Magnification of titles relative to cex.
...	additional arguments.

Details

This function is derived from plot.cumres of gof package.

Author(s)

Patrick Sfumato and Jean-Marie Boher.

References

Holst KK (2014). gof: Model-diagnostics based on cumulative residuals. R package version 0.9.1, URL https://CRAN.R-project.org/package=gof.

Examples

require(cmprsk)

#Simulating survival data with competing events
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2,200,replace=TRUE)
cov <- matrix(runif(200),nrow=200)

# Fine & Gray regression
fit.crr <- crr(ftime,fstatus,cov)

#Checking the covariates functional form assumption
k<-fcov(model=fit.crr, ftime=ftime,fstatus=fstatus,cov1=cov)

#Plotting a graphical model diagnostic
plot(k)

Example output

Loading required package: survival
Loading 'goftte' version 1.0.3
Loading required package: cmprsk

goftte documentation built on May 2, 2019, 5:12 a.m.