Execute Lasso, Ridge and and Elastic Net Linear regression model to interval variables.
sym.glm(sym.data, response = 1, method = c('cm', 'crm'),
alpha = 1, nfolds = 10, grouped = TRUE)An object of class 'cv.glmnet' is returned, which is a list with the ingredients of the cross-validation fit.
Should be a symbolic data table read with the function read.sym.table(...).
The number of the column where is the response variable in the interval data table.
'cm' to generalized Center Method and 'crm' to generalized Center and Range Method.
alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. 0<alpha<1 is the elastic net method.
Number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3
This is an experimental argument, with default TRUE, and can be ignored by most users.
Oldemar Rodriguez Rojas
Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.
sym.lm