R: Predicting from Smoothing Spline ANOVA Fits with Non-Gaussian...

predict9.gssanova {gss} | R Documentation

## Predicting from Smoothing Spline ANOVA Fits with Non-Gaussian Responses

### Description

Evaluate smoothing spline ANOVA fits with non-Gaussian responses at arbitrary points, with results on the response scale.

### Usage

    ## S3 method for class 'gssanova'
    predict9(object, newdata, ci=FALSE, level=.95, nu=NULL, ...)

### Arguments

`object` | Object of class inheriting from `"gssanova"`.
`newdata` | Data frame or model frame in which to predict.
`ci` | Flag indicating if Bayesian confidence intervals are required. Ignored for `family="polr"`.
`level` | Confidence level. Ignored when `ci=FALSE`.
`nu` | Sizes for `"nbinomial"` fits with known sizes. Ignored otherwise.
`...` | Ignored.

### Value

For `ci=FALSE`, `predict9.gssanova` returns a vector of the evaluated fit,

For `ci=TRUE`, `predict9.gssanova` returns a list of three elements.

`fit` | Vector of evaluated fit on response scale.
`lcl` | Vector of lower confidence limit on response scale.
`ucl` | Vector of upper confidence limit on response scale.

For `family="polr"`, `predict9.gssanova` returns a matrix of probabilities with each row adding up to 1.

### Note

For mixed-effect models through `gssanova` or `gssanova1`, 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.

Unlike on the link scale, partial sums make no sense on the response scale, so all terms are forced in here.

### References

Gu, C. (2013), Smoothing Spline ANOVA Models (2nd Ed). New York: Springer-Verlag.

### See Also

Fitting functions `gssanova`, `gssanova1` and methods `predict.ssanova`, `summary.gssanova`, `project.gssanova`, `fitted.gssanova`.

[Package gss version 2.2-2 Index]