setLassoLogisticRegression: Create setting for lasso logistic regression

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

Create setting for lasso logistic regression

Description

Create setting for lasso logistic regression

Usage

setLassoLogisticRegression(
  variance = 0.01,
  seed = NULL,
  includeCovariateIds = c(),
  noShrinkage = c(0),
  threads = -1,
  forceIntercept = F,
  upperLimit = 20,
  lowerLimit = 0.01,
  tolerance = 2e-06,
  maxIterations = 3000,
  priorCoefs = NULL
)

Arguments

variance	Numeric: prior distribution starting variance
seed	An option to add a seed when training the model
includeCovariateIds	a set of covariate IDS to limit the analysis to
noShrinkage	a set of covariates whcih are to be forced to be included in the final model. default is the intercept
threads	An option to set number of threads when training model
forceIntercept	Logical: Force intercept coefficient into prior
upperLimit	Numeric: Upper prior variance limit for grid-search
lowerLimit	Numeric: Lower prior variance limit for grid-search
tolerance	Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence
maxIterations	Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
priorCoefs	Use coefficients from a previous model as starting points for model fit (transfer learning)

Examples

model.lr <- setLassoLogisticRegression()

OHDSI/PatientLevelPrediction documentation built on Sept. 15, 2024, 6:38 a.m.