PomaLasso {POMA} | R Documentation

## Lasso, Ridge and Elasticnet Regularized Generalized Linear Models for Binary Outcomes

### Description

PomaLasso() is an implementation of the lasso, ridge and elasticnet regression from `glmnet` package for binary outcomes.

### Usage

    PomaLasso(
      data,
      alpha = 1,
      ntest = NULL,
      nfolds = 10,
      lambda = NULL,
      labels = FALSE
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`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 `nlambda` and `lambda.min.ratio`. See `?glmnet::glmnet()`.
`labels` | Logical indicating if feature names should be plotted in coefficient plot or not. Default is FALSE.

### Value

A list with all results including plots, data frames and the resulting prediction model.

### Author(s)

Pol Castellano-Escuder

### References

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/.

### Examples

    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)

[Package POMA version 1.0.0 Index]