biglasso: cv.biglasso – R documentation

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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),
  eval.metric = c("default", "MAPE"),
  ncores = parallel::detectCores(),
  ...,
  nfolds = 5,
  seed,
  cv.ind,
  trace = FALSE
)

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`.
`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 misclassification error. "MAPE", for linear regression only, is the Mean Absolute Percentage Error.
`ncores`	The number of cores to use for parallel execution across a cluster created by the `parallel` package. (This is different from `ncores` in `biglasso`, which is the number of OpenMP threads.)
`...`	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.

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 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 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.
`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

Maintainer: Yaohui Zeng <yaohui.zeng@gmail.com>

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

Extending Lasso Model Fitting to Big Data

v1.4.1

GPL-3

Authors

Yaohui Zeng [aut,cre], Chuyi Wang [aut,cre], Patrick Breheny [ctb]

Initial release

2021-01-29