sym.glm {RSDA} | R Documentation |
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)
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. |
An object of class 'cv.glmnet' is returned, which is a list with the ingredients of the cross-validation fit.
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