cv.rgam: Cross-validation for reluctant generalized additive model...
In relgam: Reluctant Generalized Additive Models

Description Usage Arguments Details Value Examples

Does k-fold cross-validation for rgam.

cv.rgam(x, y, lambda = NULL, family = c("gaussian", "binomial",
  "poisson", "cox"), offset = NULL, init_nz, gamma, nfolds = 10,
  foldid = NULL, keep = FALSE, parallel = FALSE, verbose = TRUE,
  ...)

`x`	Input matrix, of dimension `nobs x nvars`; each row is an observation vector.
`y`	Response `y` as in `rgam`.
`lambda`	A user-supplied `lambda` sequence. Typical usage is to have the program compute its own `lambda` sequence; supplying a value of lambda overrides this.
`family`	Response type. Either `"gaussian"` (default) for linear regression, `"binomial"` for logistic regression, `"poisson"` for Poisson regression or `"cox"` for Cox regression.
`offset`	Offset vector as in `rgam`.
`init_nz`	A vector specifying which features we must include when computing the non-linear features. Default is to construct non-linear features for all given features.
`gamma`	Scale factor for non-linear features (vs. original features), to be between 0 and 1. Default is 0.8 if `init_nz = c()`, 0.6 otherwise.
`nfolds`	Number of folds for CV (default is 10). Although `nfolds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `nfolds = 4`.
`foldid`	An optional vector of values between 1 and `nfolds` identifying what fold each observation is in. If supplied, `nfolds` can be missing.
`keep`	If `keep = TRUE`, a prevalidated array is returned containing fitted values for each observation at each value of lambda. This means these fits are computed with this observation and the rest of its fold omitted. Default is `FALSE`.
`parallel`	If TRUE, use parallel foreach to fit each fold. Must register parallel before hand, such as doMC or others. Note that this also passes `parallel = TRUE` to the `rgam()` call within each fold. Default is FALSE.
`verbose`	Print information as model is being fit? Default is `TRUE`.
`...`	Other arguments that can be passed to `rgam`.

The function runs rgam nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed.

Note that cv.rgam only does cross-validation for lambda but not for the degrees of freedom hyperparameter.

An object of class "cv.rgam".

`glmfit`	A fitted `rgam` object for the full data.
`lambda`	The values of `lambda` used in the fits.
`nzero_feat`	The number of non-zero features for the model `glmfit`.
`nzero_lin`	The number of non-zero linear components for the model `glmfit`.
`nzero_nonlin`	The number of non-zero non-linear components for the model `glmfit`.
`fit.preval`	If `keep=TRUE`, this is the array of prevalidated fits.
`cvm`	The mean cross-validated error: a vector of length `length(lambda)`.
`cvse`	Estimate of standard error of `cvm`.
`cvlo`	Lower curve = `cvm - cvsd`.
`cvup`	Upper curve = `cvm + cvsd`.
`lambda.min`	The value of `lambda` that gives minimum `cvm`.
`lambda.1se`	The largest value of `lambda` such that the CV error is within one standard error of the minimum.
`foldid`	If `keep=TRUE`, the fold assignments used.
`name`	Name of error measurement used for CV.
`call`	The call that produced this object.

set.seed(1)
n <- 100; p <- 20
x <- matrix(rnorm(n * p), n, p)
beta <- matrix(c(rep(2, 5), rep(0, 15)), ncol = 1)
y <- x %*% beta + rnorm(n)

cvfit <- cv.rgam(x, y)

# specify number of folds
cvfit <- cv.rgam(x, y, nfolds = 5)