Create modelSettings for lasso logistic regression
setLassoLogisticRegression(
variance = 0.01,
seed = NULL,
includeCovariateIds = c(),
noShrinkage = c(0),
threads = -1,
forceIntercept = FALSE,
upperLimit = 20,
lowerLimit = 0.01,
tolerance = 2e-06,
maxIterations = 3000,
priorCoefs = NULL
)modelSettings object
Numeric: prior distribution starting variance
An option to add a seed when training the model
a set of covariateIds to limit the analysis to
a set of covariates whcih are to be forced to be included in in the final model. Default is the intercept
An option to set number of threads when training model.
Logical: Force intercept coefficient into prior
Numeric: Upper prior variance limit for grid-search
Numeric: Lower prior variance limit for grid-search
Numeric: maximum relative change in convergence criterion from from successive iterations to achieve convergence
Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
Use coefficients from a previous model as starting points for model fit (transfer learning)
modelLasso <- setLassoLogisticRegression(seed=42)
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