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

summary.gssanova {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 '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 components. 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.

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.