predict.segmented: Predict method for segmented model fits

In segmented: Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)

Predict method for segmented model fits

Description

Returns predictions and optionally associated quantities (standard errors or confidence intervals) from a fitted segmented model object.

Usage

## S3 method for class 'segmented'
predict(object, newdata, se.fit=FALSE, interval=c("none","confidence", "prediction"), 
            type = c("link", "response"), na.action=na.omit, level=0.95, .coef=NULL, ...)

Arguments

object	a fitted segmented model coming from segmented.(g)lm or segreg.
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
se.fit	Logical. Should the standard errors be returned?
interval	Which interval? See predict.lm
type	Predictions on the link or response scale? Only if object is a segmented glm.
na.action	How to deal with missing data, if newdata include them.
level	The confidence level.
.coef	The regression parameter estimates. If unspecified (i.e. NULL), it is computed internally by coef().
...	further arguments.

Details

Basically predict.segmented builds the right design matrix accounting for breakpoint and passes it to predict.lm or predict.glm depending on the actual model fit object.

Value

predict.segmented produces a vector of predictions with possibly associated standard errors or confidence intervals. See predict.lm or predict.glm.

Warning

For segmented glm fits with offset, predict.segmented returns the fitted values including the offset.

Author(s)

Vito Muggeo

See Also

segreg, segmented, plot.segmented, broken.line, predict.lm, predict.glm

Examples

n=10
x=seq(-3,3,l=n)
set.seed(1515)
y <- (x<0)*x/2 + 1 + rnorm(x,sd=0.15)
segm <- segmented(lm(y ~ x), ~ x, psi=0.5)
predict(segm,se.fit = TRUE)$se.fit

#wrong (smaller) st.errors (assuming known the breakpoint)
olm<-lm(y~x+pmax(x-segm$psi[,2],0))
predict(olm,se.fit = TRUE)$se.fit

segmented documentation built on April 12, 2025, 1:59 a.m.