CVgam {gamclass} | R Documentation |
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 |
data |
data frame that supplies the data |
nfold |
Number of cross-validation folds |
debug.level |
See |
method |
Fit method for GAM model. See |
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 |
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 |
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)
}