# CVgam: Cross-validation estimate of accuracy from GAM model fit In gamclass: Functions and Data for a Course on Modern Regression and Classification

## 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

 ```1 2``` ```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)

John Maindonald

## Examples

 ```1 2 3 4 5 6 7 8``` ```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) } ```

### Example output

```Loading required package: sp