predict.ssanova {gss}R Documentation

Predicting from Smoothing Spline ANOVA Fits

Description

predict.ssanova evaluates a smoothing spline ANOVA fit at arbitrary data points as specified in newdata. The model terms to be included in the evaluation are specified via include, which defaults to all terms in the model.

With the flag se.fit=TRUE, the standard errors of the fit are also calculated, which are to be used to construct Bayesian confidence intervals of the fit.

Usage

predict[.ssanova](obj, newdata, se.fit=FALSE, include=obj$terms$labels)

Arguments

obj an object of class "ssanova".
newdata a data frame or model frame.
se.fit a logical flag.
include a list of model terms to be included in the prediction. The partial and offset terms, if present, are specified by "partial" and "offset", respectively.

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 a vector of the evaluated fit.
se.fit a vector of the corresponding standard errors.

Author(s)

Chong Gu, chong@stat.purdue.edu

See Also

The model fitting function ssanova and the summarizing function summary.ssanova.

Examples

## Fit a model with thin-plate marginals, where geog is 2-D
data(lake.acid)
fit <- ssanova(ph~log(cal)*geog,"tp",lake.acid)

## 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)


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