summary.gssanova: Assessing Smoothing Spline ANOVA Fits with Non-Gaussian...

In gss: General Smoothing Splines

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

call	Fitting call.
family	Error distribution.
alpha	Parameter used to define cross-validation in model fitting.
fitted	Fitted values on the link scale.
dispersion	Assumed or estimated dispersion parameter.
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.
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 gssanova and methods predict.ssanova, project.gssanova, fitted.gssanova.

gss documentation built on Aug. 16, 2023, 9:07 a.m.