Description Usage Arguments Value
big.simplelasso
estimates a model using LASSO and
returns a sparse structure.
Cross-validation can be used to select best covariates
combination. The dataframe must be a C++ pointer
1 2 3 | big.simplelasso(X, yvar = "incidence", labelvar = c("cancer", "age",
"Country_Transco", "year", "area.x", "area.y"), crossvalidation = T,
nfolds = 10, returnplot = T, ncores = 1)
|
X |
A |
yvar |
Column name for the explained variable |
labelvar |
Additional variables providing information but that should not be used as explanatory variables |
crossvalidation |
Should cross-validation be performed? TRUE or FALSE |
nfolds |
Number of folds for cross-validation. Ignored if
|
ncores |
Number of cores to used for computation. If |
A list of three elements.
output$model
returns the model.
output$plot
returns a plot. If crossvalidation = F
,
coefficients values when $\lambda$ penalization term
evolves is represented.
If crossvalidation = T
, the RMSE is represented with respect to the
number of variables with non-zero weight
output$coeff
returns the coefficient values returned by the LASSO
(or the coefficients of the RMSE-minimizing model if crossvalidation = T
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.