Predict method for stepmented model fits

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

Usage

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

Arguments

object: a fitted stepmented model coming from stepmented.lm or stepmented.glm.
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 stepmented 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().
.vcov: The estimate covariance matrix. If unspecified (i.e. NULL), it is computed internally by vcov.stepmented().
apprx.fit: The approximation of the \((x>\hat\psi)\) used to compute the predictions/fitted values of the piece-wise relationships.
apprx.se: The same abovementioned approximation to compute the standard error.
...: further arguments, for instance k to be passed to vcov.stepmented.

Details

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

Author

Vito Muggeo

Warning

For stepmented glm fits with offset, predict.stepmented 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