segmented (version 0.5-2.1)

plot.segmented: Plot method for segmented objects

Description

Takes a fitted segmented object returned by segmented() and plots (or adds) the fitted broken-line 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=1, rev.sgn=FALSE, const=0, shade=FALSE, rug=TRUE, 
    dens.rug=FALSE, dens.col = grey(0.8), transf=I, ...)

Arguments

x

a fitted segmented object.

term

the segmented variable having the piece-wise relationship to be plotted. If there is a single segmented variable in the fitted model x, term can be omitted.

add

when TRUE the fitted lines are added to the current device.

res

when TRUE the fitted lines are plotted along with corresponding partial residuals. See Details.

conf.level

If greater than zero, it means the confidence level at which the pointwise confidence itervals have to be plotted.

interc

If TRUE the computed segmented components include the model intercept (if it exists).

link

when TRUE (default), the fitted lines are plotted on the link scale, otherwise they are tranformed on the response scale before plotting. Ignored for linear segmented fits.

res.col

when res=TRUE it means the color of the points representing the partial residuals.

rev.sgn

when TRUE it is assumed that current term is `minus' the actual segmented variable, therefore the sign is reversed before plotting. This is useful when a null-constraint has been set on the last slope.

const

constant to add to each fitted segmented relationship (on the scale of the linear predictor) before plotting.

shade

if TRUE and conf.level>0 it produces shaded regions (in grey color) for the pointwise confidence intervals embracing the fitted segmented line.

rug

when TRUE (default) then the covariate values are displayed as a rug plot at the foot of the plot.

dens.rug

when TRUE then smooth covariate distribution is plotted on the x-axis.

dens.col

if dens.rug=TRUE, it means the colour to be used to plot the density.

transf

A possible function to convert the fitted values before plotting. It is only effective if the fitted values refer to a linear or a generalized linear model (on the link scale) and res=FALSE.

…

other graphics parameters to pass to plotting commands: `col', `lwd' and `lty' (that can be vectors, see the example below) for the fitted piecewise lines; `ylab', `xlab', `main', `sub', `xlim' and `ylim' when a new plot is produced (i.e. when add=FALSE); `pch' and `cex' for the partial residuals (when res=TRUE).

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

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, ~z) #single segmented covariate and one breakpoint:'psi' can be omitted
par(mfrow=c(2,1))
plot(o.seg, conf.level=0.95, shade=TRUE)
points(o.seg, link=FALSE, 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)
points(o.seg,col=4, link=FALSE)