R: Lasso, Ridge and and Elastic Net Linear regression model to...

sym.glm {RSDA}

R Documentation

Lasso, Ridge and and Elastic Net Linear regression model to interval variables

Description

Execute Lasso, Ridge and and Elastic Net Linear regression model to interval variables.

Usage

sym.glm(sym.data, response = 1, method = c('cm', 'crm'),
alpha = 1, nfolds = 10, grouped = TRUE)

Arguments

`sym.data`	Should be a symbolic data table read with the function read.sym.table(...).
`response`	The number of the column where is the response variable in the interval data table.
`method`	'cm' to generalized Center Method and 'crm' to generalized Center and Range Method.
`alpha`	alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. 0<alpha<1 is the elastic net method.
`nfolds`	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
`grouped`	This is an experimental argument, with default TRUE, and can be ignored by most users.

Value

An object of class 'cv.glmnet' is returned, which is a list with the ingredients of the cross-validation fit.

Author(s)

Oldemar Rodriguez Rojas

References

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.