summary.gssanova {gss}

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.