CVgam: Cross-validation estimate of accuracy from GAM model fit

In gamclass: Functions and Data for a Course on Modern Regression and Classification

Cross-validation estimate of accuracy from GAM model fit

Description

The cross-validation estimate of accuracy is sufficiently independent of the available model fitting criteria (including Generalized Cross-validation) that it provides a useful check on the extent of downward bias in the estimated standard error of residual.

Usage

CVgam(formula, data, nfold = 10, debug.level = 0, method = "GCV.Cp",
              printit = TRUE, cvparts = NULL, gamma = 1, seed = 29)

Arguments

formula	Model formula, for passing to the gam() function
data	data frame that supplies the data
nfold	Number of cross-validation folds
debug.level	See gam for details
method	Fit method for GAM model. See gam for details
printit	Should summary information be printed?
cvparts	Use, if required, to specify the precise folds used for the cross-validation. The comparison between different models will be more accurate if the same folds are used.
gamma	See gam for details.
seed	Set seed, if required, so that results are exactly reproducible

Value

fitted	fitted values
resid	residuals
cvscale	scale parameter from cross-validation
scale.gam	scale parameter from function gam

The scale parameter from cross-validation is the error mean square)

Author(s)

John Maindonald

References

https://maths-people.anu.edu.au/~johnm/nzsr/taws.html

Examples

if(require(sp)){
library(mgcv)
data(meuse)
meuse$ffreq <- factor(meuse$ffreq)
CVgam(formula=log(zinc)~s(elev) + s(dist) + ffreq + soil,
              data = meuse, nfold = 10, debug.level = 0, method = "GCV.Cp",
              printit = TRUE, cvparts = NULL, gamma = 1, seed = 29)
}

gamclass documentation built on Aug. 21, 2023, 5:07 p.m.