R: Assessing Smoothing Spline ANOVA Fits

summary.ssanova {gss}

R Documentation

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 components. 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.

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.