segmented (version 2.2-1)

plot.segmented: Plot method for segmented objects

Description

Takes a fitted segmented object returned by segmented() and plots (or adds) the fitted broken-line relationship for the selected segmented term.

Usage

# S3 method for segmented
plot(x, term, add=FALSE, res=FALSE, conf.level=0, interc=TRUE, link=TRUE, 
    res.col=grey(.15, alpha = .4), rev.sgn=FALSE, const=NULL, shade=FALSE, rug=!add, 
    dens.rug=FALSE, dens.col = grey(0.8), transf=I, isV=FALSE, is=FALSE, var.diff=FALSE, 
    p.df="p", .vcov=NULL, .coef=NULL, prev.trend=FALSE, smoos=NULL, hide.zeros=FALSE, 
    leg="topleft", psi.lines=FALSE, ...)

Value

None.

Details

Produces (or adds to the current device) the fitted segmented relationship between the response and the selected term. If the fitted model includes just a single `segmented' variable, term may be omitted.
The partial residuals are computed as `fitted + residuals', where `fitted' are the fitted values of the segmented relationship relevant to the covariate specified in term. Notice that for GLMs the residuals are the response residuals if link=FALSE and the working residuals if link=TRUE.

See Also

segmented to fit the model, lines.segmented to add the estimated breakpoints on the current plot. points.segmented to add the joinpoints of the segmented relationship. predict.segmented to compute standard errors and confidence intervals for predictions from a "segmented" fit.

Examples

set.seed(1234)
z<-runif(100)
y<-rpois(100,exp(2+1.8*pmax(z-.6,0)))
o<-glm(y~z,family=poisson)
o.seg<-segmented(o) #single segmented covariate and one breakpoint: 'seg.Z' and 'psi' can be omitted
par(mfrow=c(1,2))
plot(o.seg, conf.level=0.95, shade=TRUE)
points(o.seg, link=TRUE, col=2)
## new plot
plot(z,y)
## add the fitted lines using different colors and styles..
plot(o.seg,add=TRUE,link=FALSE,lwd=2,col=2:3, lty=c(1,3))
lines(o.seg,col=2,pch=19,bottom=FALSE,lwd=2) #for the CI for the breakpoint
points(o.seg,col=4, link=FALSE)
## using the options 'is', 'isV', 'shade' and 'col.shade'.
par(mfrow=c(1,2))
plot(o.seg, conf.level=.9, is=TRUE, isV=TRUE, col=1, shade = TRUE, col.shade=2)
plot(o.seg, conf.level=.9, is=TRUE, isV=FALSE, col=2, shade = TRUE, res=TRUE, res.col=4, pch=3)