segmented: plot.segmented – R documentation

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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=!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, ...)

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` the covariate values are displayed as a rug plot at the foot of the plot. Default is to `!add`.
`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 `res=FALSE`. If `res=TRUE` any transformation is ignored.
`isV`	logical value (to be passed to `broken.line`). Ignored if `conf.level=0`
`is`	logical value (to be passed to `broken.line`) indicating if the covariance matrix based on the induced smoothing should be used. Ignored if `conf.level=0`
`var.diff`	logical value to be passed to `summary.segmented` to compute dthe standard errors of fitted values (if `conf.level>0`).
`p.df`	degrees of freedom when `var.diff=TRUE`, see `summary.segmented`
`.vcov`	The full covariance matrix of estimates to be used when `conf.level>0`. If unspecified (i.e. `NULL`), the covariance matrix is computed internally by `vcov.segmented`.
`.coef`	The regression parameter estimates. If unspecified (i.e. `NULL`), it is computed internally by `coef()`.
`prev.trend`	logical. If `TRUE` dashed lines corresponding to the ‘previous’ trends (i.e. the trends if the breakpoints would not have occurred) are also drawn.
`smoos`	logical, indicating if the residuals (provided that `res=TRUE`) will be drawn using a smoothed scatterplot. If `NULL` (default) the smoothed scatterplot will be employed when the number of observation is larger than 10000.
`...`	other graphics parameters to pass to plotting commands: ‘col’, ‘lwd’ and ‘lty’ (that can be vectors and are recycled if necessary, see the example below) for the fitted piecewise lines; ‘ylab’, ‘xlab’, ‘main’, ‘sub’, ‘cex.axis’, ‘cex.lab’, ‘xlim’ and ‘ylim’ when a new plot is produced (i.e. when `add=FALSE`); ‘pch’ and ‘cex’ for the partial residuals (when `res=TRUE`, `res.col` is for the color); `col.shade` for the shaded regions (provided that `shade=TRUE` and `conf.level>0`).

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.

Value

None.

Note

For models with offset, partial residuals on the response scale are not defined. Thus plot.segmented does not work when link=FALSE, res=TRUE, and the fitted model includes an offset.

Author(s)

Vito M. R. Muggeo

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)

segmented

Regression Models with Break-Points / Change-Points Estimation

v1.3-4

GPL

Authors

Vito M. R. Muggeo [aut, cre] (<https://orcid.org/0000-0002-3386-4054>)

Initial release

2021-04-28