Description Usage Arguments Value
simplelasso
estimates a model using LASSO and
returns a sparse structure.
Crossvalidation can be used to select best covariates
combination
1 2  simplelasso(df, yvar = "incidence", crossvalidation = T, nfolds = 10,
include.intercept = TRUE, lag.order = NULL)

df 
Dataframe 
crossvalidation 
Should crossvalidation be performed? TRUE or FALSE 
nfolds 
Number of folds for crossvalidation. Ignored if

simplify 
Boolean indicating whether some factor variables should be dropped 
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 nonzero weight
output$coeff
returns the coefficient values returned by the LASSO
(or the coefficients of the RMSEminimizing model if crossvalidation = T
)
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