Description Usage Arguments Details Value See Also Examples

Fits a lasso model and a lasso followed by a stepAIC algorithm.

1 | ```
lassomodel(Data, standardize = TRUE, measure = c("deviance"), nfold = 5)
``` |

`Data` |
a data frame, as a first column should have the response variable y |

`standardize` |
Logical flag for variable standardization, prior to fitting the model. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. |

`measure` |
loss to use for cross-validation. measure="auc" is for two-class logistic regression only, and gives area under the ROC curve. measure="deviance", uses the deviance for logistic regression. |

`nfold` |
number of folds - default is 5. 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 |

the function lassomodel is a wrapper function over the glmnet::glmnet. The parameter lambda is tuned by 10-fold cross-validation with the glmnet::cv.glmnet function. The selected lambda is the one that gives either the minimum deviance (measure="deviance") or the maximum auc (measure="auc") or minimum misclassification error (measure="class")

a list with the coefficients in the final model for the lasso fit and also for the lasso followed by stepAIC.

1 2 3 4 5 6 7 8 9 10 | ```
set.seed(14)
beta <- c(3, 2, -1.6, -4)
noise <- 5
simData <- SimData(N=100, beta=beta, noise=noise, corr=FALSE)
lassofit <- lassomodel(Data=simData, measure="auc")
lassofit
lassofit2 <- lassomodel(Data=simData, measure="deviance")
lassofit2
``` |

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