cv.fwelnet: Cross-validation for fwelnet

In kjytay/fwelnet: Feature-Weighted Elastic Net

Description

Does k-fold cross-validation for fwelnet.

Usage

cv.fwelnet(
  x,
  y,
  z,
  family = c("gaussian", "binomial"),
  lambda = NULL,
  type.measure = c("mse", "deviance", "class", "auc", "mae"),
  nfolds = 10,
  foldid = NULL,
  keep = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x	x matrix as in fwelnet.
y	y matrix as in fwelnet.
z	z matrix as in fwelnet.
family	Response type. Either "gaussian" (default) for linear regression or "binomial" for logistic regression.
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.
type.measure	Loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure="deviance", which uses squared-error for gaussian models (a.k.a type.measure="mse" there) and deviance for logistic regression. type.measure="class" applies to binomial logistic regression only, and gives misclassification error. type.measure="auc" is for two-class logistic regression only, and gives area under the ROC curve. type.measure="mse" or type.measure="mae" (mean absolute error) can be used by all models.
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 = 3.
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.
verbose	Print information as model is being fit? Default is FALSE.
...	Other arguments that can be passed to fwelnet.

Details

This function runs fwelnet nfolds+1 times: the first to get the lambda sequence, and the remaining nfolds times to compute the fit with each of the folds omitted. The error is accumulated, and the mean error and standard deviation over the folds is computed. Note that cv.pcLasso does NOT search for values of alpha. A specific value of alpha should be supplied.

Value

An object of class "cv.fwelnet", which is a list with the ingredients of the cross-validation fit.

glmfit	A fitted fwelnet object for the full data.
lambda	The values of lambda used in the fits.
nzero	The number of non-zero coefficients in 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).
cvsd	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.

Examples

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)
z <- cbind(1, abs(beta) + rnorm(p))

cvfit1 <- cv.fwelnet(x, y, z)

# change no. of CV folds
cvfit2 <- cv.fwelnet(x, y, z, nfolds = 5)
# specify which observations are in each fold
foldid <- sample(rep(seq(5), length = length(y)))
cvfit3 <- cv.fwelnet(x, y, z, foldid = foldid)
# keep=TRUE to have pre-validated fits and foldid returned
cvfit4 <- cv.fwelnet(x, y, z, keep = TRUE)

kjytay/fwelnet documentation built on June 9, 2020, 1:39 p.m.