R: Assessing Smoothing Spline ANOVA Fits

summary.ssanova {gss} | R Documentation

## Assessing Smoothing Spline ANOVA Fits

### Description

Calculate various summaries of smoothing spline ANOVA fits.

### Usage

    ## S3 method for class 'ssanova'
    summary(object, diagnostics=FALSE, ...)
    ## S3 method for class 'ssanova0'
    summary(object, diagnostics=FALSE, ...)
    ## S3 method for class 'ssanova9'
    summary(object, diagnostics=FALSE, ...)

### Arguments

`object` | Object of class `"ssanova"`.
`diagnostics` | Flag indicating if diagnostics are required.
`...` | Ignored.

### Value

`summary.ssanova` returns a list object of `class` `"summary.ssanova"` consisting of the following components. The entries `pi`, `kappa`, `cosines`, and `roughness` are only calculated if `diagnostics=TRUE`; see the reference below for details concerning the diagnostics.

`call` | Fitting call.
`method` | Method for smoothing parameter selection.
`fitted` | Fitted values.
`residuals` | Residuals.
`sigma` | Assumed or estimated error standard deviation.
`r.squared` | Fraction of "explained variance" by the fitted model.
`rss` | Residual sum of squares.
`penalty` | Roughness penalty associated with the fit.
`pi` | "Percentage decomposition" of "explained variance" into model terms.
`kappa` | Concurvity diagnostics for model terms. Virtually the square roots of variance inflation factors of a retrospective linear model.
`cosines` | Cosine diagnostics for practical significance of model terms.
`roughness` | Percentage decomposition of the roughness penalty `penalty` into model terms.

### Author(s)

Chong Gu, chong@stat.purdue.edu

### References

Gu, C. (1992), Diagnostics for nonparametric regression models with additive terms. Journal of the American Statistical Association, 87, 1051–1058.

### See Also

Fitting functions `ssanova`, `ssanova0` and methods `predict.ssanova`, `project.ssanova`, `fitted.ssanova`.

[Package gss version 2.1-7 Index]