| predict.ssanova {gss} | R Documentation |
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.
## 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), ...)
object |
Object of class inheriting from |
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 |
... |
Ignored. |
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. |
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, where the as-is function I(...) preserves the
integrity of the Z matrix in data frame.
Chong Gu, chong@stat.purdue.edu
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.
Fitting functions ssanova, ssanova0,
gssanova, gssanova0 and
methods summary.ssanova,
summary.gssanova, summary.gssanova0,
project.ssanova, fitted.ssanova.
## 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)