Create setting for lasso logistic regression
setLassoLogisticRegression( variance = 0.01, seed = NULL, includeCovariateIds = c(), noShrinkage = c(0), threads = -1, useCrossValidation = TRUE, upperLimit = 20, lowerLimit = 0.01 )
a single value used as the starting value for the automatic lambda search
An option to add a seed when training the model
a set of covariate IDS to limit the analysis to
a set of covariates whcih are to be forced to be included in the final model. default is the intercept
An option to set number of threads when training model
Set this to FALSE if you want to train a LR with a preset varience
Upper prior variance limit for grid-search
Lower prior variance limit for grid-search
# NOT RUN { model.lr <- setLassoLogisticRegression() # }
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