segmented (version 0.5-1.1)

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 class '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), show.gap=FALSE,  ...)

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.

show.gap

when FALSE the (possible) gaps between the fitted lines at the estimated breakpoints are hidden. When bootstrap restarting has been employed (default in segmented), show.gap is meaningless as the gap c

...

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

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. Due to the parameterization of the segmented terms, sometimes the fitted lines may not appear to join at the estimated breakpoints. If this is the case, the apparent `gap' would indicate some lack-of-fit. However, since version 0.2-9.0, the gap coefficients are set to zero by default (see argument gap in in seg.control). The partial residuals are computed as `fitted + residuals', where `fitted' are the fitted values of the segmented relationship. Notice that for GLMs the residuals are the response residuals if link=FALSE and the working residuals weighted by the IWLS weights 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,seg.Z=~z,psi=list(z=.5))
par(mfrow=c(2,1))
plot(o.seg, conf.level=0.95, shade=TRUE)
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)