plot.threg {threg}

Plot curves of the estimated survival, hazard or density functions for threshold regression model

Description

This function can be used to display the graphs of the estimated survival, hazard or density functions at different levels of a factor predictor variable which has been included in the threshold regression by threg() function.

Usage

## S3 method for class 'threg'
plot(x,var,scenario,graph,nolegend=0,nocolor=0,...)

Arguments

x

a threg object.

var

specifies the categorical variable for each level of which the curves are plotted. Such categorical variable must be a factor variable that has been used in threg() that returns the threg object.

scenario

specifies a scenario where the predicted plots are based on.

graph

specifies the type of curves to be generated. The “hz” option is to plot hazard function curves, the “sv” option is to plot survival function curves, and the “ds” option is to plot density function curves.

nolegend

set the “nolegend” argument to 1 if users do not want the “threg” package to generate legends for the picture. Note that even if “nolegend” is set to 1, users can still generate legends by themselves after the picture is generated, by using the “legend” function in R.

nocolor

set the “nocolor” argument to 1 if users want to depict all curves in black.

...

for future methods

Examples

#load the data "lkr"
data("lkr")

#Transform the "treatment2" variable into factor variable "f.treatment2" .
lkr$f.treatment2=factor(lkr$treatment2)

#fit the threshold regression model on the factor variable "f.treatment2", 
fit<-threg(Surv(weeks, relapse)~ f.treatment2|f.treatment2,data = lkr)
fit

#generate the predicted survival curves for the drug B group and 
#the standard group.
plot(fit,var=f.treatment2,graph=sv,nolegend=1,nocolor=1)
legend(20, 1, c("Standard","Drug B"), lty = 1:2) 



#load the data "bmt"
data("bmt")

#Transform the "group" and "fab" variables into factor variables 
#"f.group" and "f.fab".
bmt$f.group=factor(bmt$group)
bmt$f.fab=factor(bmt$fab)

#fit a threshold regression model on the "bmt" dataset, by using "recipient_age" and 
#"f.fab" as the predictors for ln(y0), and "f.group" and "f.fab" as predictors for mu.
fit<-threg(Surv(time, indicator)~ recipient_age+f.fab|f.group+f.fab, data = bmt)
fit

#fit the same model as above, but additionally overlay curves of survival functions 
#corresponding to different levels of "f.group'.
plot.threg(fit,var=f.group,scenario=recipient_age(18)+f.fab1(0),graph=sv,nocolor=1)


#fit the same model as above, but additionally overlay curves of hazard functions 
#corresponding to different levels of "f.group'.
plot.threg(fit,var=f.group,scenario=recipient_age(18)+f.fab1(0),graph=hz,nocolor=1)

#fit the same model as above, but additionally overlay curves of probability density 
#functions corresponding to different levels of "f.group'.
plot.threg(fit,var=f.group,scenario=recipient_age(18)+f.fab1(0),graph=ds,nocolor=1)