PomaLasso {POMA} | R Documentation |
PomaLasso() is an implementation of the lasso, ridge and elasticnet regression from glmnet
package for binary outcomes.
PomaLasso( data, alpha = 1, ntest = NULL, nfolds = 10, lambda = NULL, labels = FALSE )
data |
A MSnSet object. First |
alpha |
Elasticnet mixing parameter. alpha = 1 is the lasso penalty and alpha = 0 is the ridge penalty. This value must be between 0 and 1. |
ntest |
Numeric indicating the percentage of observations that will be used as test set. Default is NULL (no test set). |
nfolds |
Number of folds for CV (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. |
lambda |
A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on |
labels |
Logical indicating if feature names should be plotted in coefficient plot or not. Default is FALSE. |
A list with all results including plots, data frames and the resulting prediction model.
Pol Castellano-Escuder
Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL http://www.jstatsoft.org/v33/i01/.
data("st000336") # lasso st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOutliers() %>% PomaLasso() # elasticnet st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOutliers() %>% PomaLasso(alpha = 0.5) # ridge st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOutliers() %>% PomaLasso(alpha = 0)