R: Assessing Smoothing Spline ANOVA Fits with Non-Gaussian...

summary.gssanova0 {gss} | R Documentation

## Assessing Smoothing Spline ANOVA Fits with Non-Gaussian Responses

### Description

Calculate various summaries of smoothing spline ANOVA fits with non-Gaussian responses.

### Usage

    ## S3 method for class 'gssanova0'
    summary(object, diagnostics=FALSE, ...)

### Arguments

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

### Details

Similar to the iterated weighted least squares fitting of `glm`, penalized likelihood regression fit can be calculated through iterated penalized weighted least squares.

The diagnostics are based on the "pseudo" Gaussian response model behind the weighted least squares problem at convergence.

### Value

`summary.gssanova0` returns a list object of `class` `"summary.gssanova0"` consisting of the following elements. The entries `pi`, `kappa`, `cosines`, and `roughness` are only calculated if `diagnostics=TRUE`.

`call` | Fitting call.
`family` | Error distribution.
`method` | Method for smoothing parameter selection.
`dispersion` | Assumed or estimated dispersion parameter.
`iter` | Number of performance-oriented iterations performed.
`fitted` | Fitted values on the link scale.
`residuals` | Working residuals on the link scale.
`rss` | Residual sum of squares.
`dev.resid` | Deviance residuals.
`deviance` | Deviance of the fit.
`dev.null` | Deviance of the null model.
`alpha` | Estimated size for `family="nbinomial"` with one column responses. Estimated inverse scale of log life time for `family="nbinomial"`, `"lognorm"`, or `"loglogis"`.
`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.

### References

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

### See Also

Fitting function `gssanova0` and methods `predict.ssanova0`, `fitted.gssanova`.

[Package gss version 2.2-2 Index]