cv.biglasso: Cross-validation for biglasso

Description

Perform k-fold cross validation for penalized regression models over a grid of values for the regularization parameter lambda.

Usage

cv.biglasso(
  X,
  y,
  row.idx = 1:nrow(X),
  family = c("gaussian", "binomial", "cox", "mgaussian"),
  eval.metric = c("default", "MAPE", "auc", "class"),
  ncores = parallel::detectCores(),
  ...,
  nfolds = 5,
  seed,
  cv.ind,
  trace = FALSE,
  grouped = TRUE
)

Value

An object with S3 class "cv.biglasso" which inherits from class "cv.ncvreg". The following variables are contained in the class:

cve: The error for each value of lambda, averaged across the cross-validation folds.

cvse

The estimated standard error associated with each value of for cve.

lambda

The sequence of regularization parameter values along which the cross-validation error was calculated.

fit

The fitted biglasso object for the whole data.

min

The index of lambda corresponding to lambda.min.

lambda.min

The value of lambda with the minimum cross-validation error.

lambda.1se

The largest value of lambda for which the cross-validation error is at most one standard error larger than the minimum cross-validation error.

null.dev

The deviance for the intercept-only model.

If family="binomial", the cross-validation prediction error for each value of lambda.

cv.ind

Same as above.

Arguments

X

The design matrix, without an intercept, as in biglasso().

y

The response vector, as in biglasso.

row.idx

The integer vector of row indices of X that used for fitting the model. as in biglasso.

family

Either "gaussian", "binomial", "cox" or "mgaussian" depending on the response. "cox" and "mgaussian" are not supported yet.

eval.metric

The evaluation metric for the cross-validated error and for choosing optimal lambda. "default" for linear regression is MSE (mean squared error), for logistic regression is binomial deviance. "MAPE", for linear regression only, is the Mean Absolute Percentage Error. "auc", for binary classification, is the area under the receiver operating characteristic curve (ROC). "class", for binary classification, gives the misclassification error.

ncores

The number of cores to use when fitting the cross-validation folds in parallel. Two subtleties worth noting:

X must be file-backed in order to take advantage of this option (fold workers are separate processes that reattach X from its backing file).
ncores is also passed to biglasso() for the initial, full-data fit ().

...

Additional arguments to biglasso.

nfolds

The number of cross-validation folds. Default is 5.

seed

The seed of the random number generator in order to obtain reproducible results.

cv.ind

Which fold each observation belongs to. By default the observations are randomly assigned by cv.biglasso.

trace

If set to TRUE, cv.biglasso will inform the user of its progress by announcing the beginning of each CV fold. Default is FALSE.

grouped

Whether to calculate CV standard error (cvse) over CV folds (TRUE), or over all cross-validated predictions. Ignored when eval.metric is 'auc'.

Details

The function calls biglasso nfolds times, each time leaving out 1/nfolds of the data. The cross-validation error is based on the residual sum of squares when family="gaussian" and the binomial deviance when family="binomial".

The S3 class object cv.biglasso inherits class ncvreg::cv.ncvreg(). So S3 functions such as "summary", "plot" can be directly applied to the cv.biglasso object.

Examples

Run this code

if (FALSE) {
## cv.biglasso
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X)

## logistic regression
cvfit <- cv.biglasso(X.bm, y, family = "binomial", seed = 1234, ncores = 2)
par(mfrow = c(2, 2))
plot(cvfit, type = "all")
summary(cvfit)
}