Opened 13 days ago
Provide full functionality of glmnet package
|Reported by:||gkronber||Owned by:|
|Priority:||medium||Milestone:||HeuristicLab 3.3.x Backlog|
So far we have only provided a wrapper for elastic net regression. However, the glmnet package provides a lot of additional functionality. Since we already include the package we could rather easily support the full functionality of the glmnet package.
Excepts from the glmnet documentation: "Can deal with all shapes of data, including very large sparse data matrices. Fits linear, logistic and multinomial, poisson, and Cox regression models."
"y response variable. Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For either "binomial" or "multinomial", if y is presented as a vector, it will be coerced into a factor. For family="cox", y should be a two-column matrix with columns named ’time’ and ’status’. The latter is a binary variable, with ’1’ indicating death, and ’0’ indicating right censored. The function Surv() in package survival produces such a matrix. For family="mgaussian", y is a matrix of quantitative responses."