predict.segmented: Predict method for segmented model fits In segmented: Regression Models with Break-Points / Change-Points Estimation

Description

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

Usage

 ```1 2``` ```## S3 method for class 'segmented' predict(object, newdata, ...) ```

Arguments

 `object` a fitted segmented model coming from `segmented.lm` or `segmented.glm`. `newdata` An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. `...` further arguments passed to `predict.lm` or `predict.glm`. Usually these are `se.fit`, or `interval` or `type`.

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`.

Note

If `type="terms"`, `predict.segmented` returns predictions for each component of the segmented term. Namely if ‘my.x’ is the segmented variable, predictions for ‘my.x’, ‘U1.my.x’ and ‘psi1.my.x’ are returned. These are meaningless individually, however their sum provides the predictions for the segmented term.

Author(s)

Vito Muggeo

`plot.segmented`, `broken.line`, `predict.lm`, `predict.glm`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```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 ```