predict.ssanova: Predicting from Smoothing Spline ANOVA Fits

Description

Evaluate terms in a smoothing spline ANOVA fit at arbitrary points. Standard errors of the terms can be requested for use in constructing Bayesian confidence intervals.

Usage

# S3 method for ssanova
predict(object, newdata, se.fit=FALSE,
                          include=c(object$terms$labels,object$lab.p), ...)
# S3 method for ssanova0
predict(object, newdata, se.fit=FALSE,
                           include=c(object$terms$labels,object$lab.p), ...)
# S3 method for ssanova
predict1(object, contr=c(1,-1), newdata, se.fit=TRUE,
                           include=c(object$terms$labels,object$lab.p), ...)

Value

For se.fit=FALSE, predict.ssanova returns a vector of the evaluated fit.

For se.fit=TRUE, predict.ssanova returns a list consisting of the following elements.

fit: Vector of evaluated fit.
se.fit: Vector of standard errors.

Arguments

object: Object of class inheriting from "ssanova".
newdata: Data frame or model frame in which to predict.
se.fit: Flag indicating if standard errors are required.
include: List of model terms to be included in the prediction. The offset term, if present, is to be specified by "offset".
contr: Contrast coefficients.
...: Ignored.

References

Gu, C. (1992), Penalized likelihood regression: a Bayesian analysis. Statistica Sinica, 2, 255--264.

Gu, C. and Wahba, G. (1993), Smoothing spline ANOVA with component-wise Bayesian "confidence intervals." Journal of Computational and Graphical Statistics, 2, 97--117.

Kim, Y.-J. and Gu, C. (2004), Smoothing spline Gaussian regression: more scalable computation via efficient approximation. Journal of the Royal Statistical Society, Ser. B, 66, 337--356.

Examples

Run this code

## THE FOLLOWING EXAMPLE IS TIME-CONSUMING
if (FALSE) {
## Fit a model with cubic and thin-plate marginals, where geog is 2-D
data(LakeAcidity)
fit <- ssanova(ph~log(cal)*geog,,LakeAcidity)
## Obtain estimates and standard errors on a grid
new <- data.frame(cal=1,geog=I(matrix(0,1,2)))
new <- model.frame(~log(cal)+geog,new)
predict(fit,new,se=TRUE)
## Evaluate the geog main effect
predict(fit,new,se=TRUE,inc="geog")
## Evaluate the sum of the geog main effect and the interaction
predict(fit,new,se=TRUE,inc=c("geog","log(cal):geog"))
## Evaluate the geog main effect on a grid
grid <- seq(-.04,.04,len=21)
new <- model.frame(~geog,list(geog=cbind(rep(grid,21),rep(grid,rep(21,21)))))
est <- predict(fit,new,se=TRUE,inc="geog")
## Plot the fit and standard error
par(pty="s")
contour(grid,grid,matrix(est$fit,21,21),col=1)
contour(grid,grid,matrix(est$se,21,21),add=TRUE,col=2)
## Clean up
rm(LakeAcidity,fit,new,grid,est)
dev.off()
}