cv.biglasso: Cross-validation for biglasso

In biglasso: Extending Lasso Model Fitting to Big Data

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
)

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 for parallel execution of the cross-validation folds, run on a cluster created by the parallel package. (This is also supplied to the ncores argument in biglasso(), which is the number of OpenMP threads, but only for the first call of biglasso() that is run on the entire data. The individual calls of biglasso() for the CV folds are run without the ncores argument.)
...	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.

Value

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

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.
pe	If family="binomial", the cross-validation prediction error for each value of lambda.
cv.ind	Same as above.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), plot.cv.biglasso(), summary.cv.biglasso(), setupX()

Examples

## Not run: 
## 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)

## End(Not run)

biglasso documentation built on April 4, 2025, 3:16 a.m.