predict.ssanova {gss}R Documentation

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 class 'ssanova':
predict(object, newdata, se.fit=FALSE,
                include=object$terms$labels, ...)
## S3 method for class 'ssanova0':
predict(object, newdata, se.fit=FALSE,
                 include=object$terms$labels, ...)

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 partial and offset terms, if present, are to be specified by "partial" and "offset", respectively.
... Ignored.

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 components.

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

Note

To supply the partial terms for partial spline models, add a component partial=I(...) in newdata; the "as is" function I(...) is necessary when partial has more than one column.

For mixed-effect models through ssanova or gssanova, the Z matrix is set to 0 if not supplied. To supply the Z matrix, add a component random=I(...) in newdata.

Author(s)

Chong Gu, chong@stat.purdue.edu

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.

See Also

Fitting functions ssanova, ssanova0, gssanova, gssanova0 and methods summary.ssanova, summary.gssanova, summary.gssanova0, project.ssanova, fitted.ssanova.

Examples

## THE FOLLOWING EXAMPLE IS TIME-CONSUMING
## Not run: 
## 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()
## End(Not run)

[Package gss version 1.0-5 Index]