predict.ssanova {gss}

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

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.

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.

Note

For mixed-effect models through ssanova or gssanova, the Z matrix is set to 0 if not supplied. To supply the Z matrix, add an element random=I(...) in newdata, where the as-is function I(...) preserves the integrity of the Z matrix in data frame.

predict1.ssanova takes a list of data frames in newdata representing x1, x2, etc. By default, it calculates f(x1)-f(x2) along with standard errors. While pairwise contrast is the targeted application, all linear combinations can be computed.

For "gssanova" objects, the results are on the link scale. See also predict9.gssanova.

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)