R: Predicting from Smoothing Spline ANOVA Fits

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=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 components.

`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 a component `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.

### 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 2.1-7 Index]