recover_data
has been designed to
easily perform feature selection by LASSO on
pointers and import selected features as well as
additional relevant variables back in the memory
1 2 3 | recover_data(X, yvar = "incidence", labelvar = c("cancer", "age",
"Country_Transco", "year", "area.x", "area.y"), crossvalidation = T,
nfolds = 10, returnplot = F, ncores = 1)
|
X |
A |
yvar |
Name of the variable we want to
explain in |
labelvar |
Variable names of columns excluded from
the set of covariates in LASSO. All variables
included in |
crossvalidation |
Should we perform a cross-validated
LASSO ? |
nfolds |
Number of folds for cross-validation. Ignored if
|
returnplot |
Should we return a plot of the relationship
between quadratic risk and $\lambda$ parameter? |
ncores |
Number of cores used for computations.
If |
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