summary.ssanova: Assessing Smoothing Spline ANOVA Fits

In gss: General Smoothing Splines

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 elements. 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.

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.

gss documentation built on April 14, 2025, 1:09 a.m.