Predict method for segmented model fits

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.

Author

Vito Muggeo

Warning

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

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
#>          1          2          3          4          5          6          7 
#> 0.12154843 0.07957213 0.07957213 0.12154843 0.10514462 0.07893808 0.06179915 
#>          8          9         10 
#> 0.06179915 0.07893808 0.10514462 

#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
#>  [1] 0.10212152 0.07366948 0.05650195 0.06102983 0.08380529 0.06359044
#>  [7] 0.05260029 0.05650192 0.07294363 0.09566456