big.model.FElasso: Perform LASSO for feature selection and create linear...
In linogaliana/OpenCancer: Build statistical models to understand cancer causes

Perform LASSO for feature selection and create linear regression using selected features for big.matrix object

big.model.FElasso(X, yvar = "incidence", labelvar = c("cancer", "age",
  "Country_Transco", "year", "area.x", "area.y"), groupingvar = NULL,
  crossvalidation = T, nfolds = 10, ncores = 1, returnplot = T,
  relabel = FALSE)

`X`	A big.matrix object
`yvar`	Name of the explained variable in `X`
`labelvar`	Names of variables that should be excluded from the set of covariates
`groupingvar`	Variables that should be used to define independent groups. Default to `NULL` means there is no groups used
`crossvalidation`	Should we perform cross-validation
`nfolds`	Number of folds for cross validation. Ignored if `crossvalidation = F`. If `crossvalidation` equals the number of observations, a leave-one-out cross validation is performed
`ncores`	The number of OpenMP threads used for parallel computing.
`returnplot`	`TRUE` or `FALSE`. Should we return a plot of the LASSO performance depending of the $\lambda$ value
`relabel`	`TRUE` or `FALSE`. Should we present variables codes (`FALSE`) or variables labels (`TRUE`) in the regressions. If `TRUE`, `import_label()` is used to upload data from the internet. It will fail if no internet connection is available.