Create a parameter object for the function createPs
createCreatePsArgs( excludeCovariateIds = c(), includeCovariateIds = c(), maxCohortSizeForFitting = 250000, errorOnHighCorrelation = TRUE, stopOnError = TRUE, prior = createPrior("laplace", exclude = c(0), useCrossValidation = TRUE), control = createControl(noiseLevel = "silent", cvType = "auto", seed = 1, tolerance = 2e-07, cvRepetitions = 10, startingVariance = 0.01) )
Exclude these covariates from the propensity model.
Include only these covariates in the propensity model.
If the target or comparator cohort are larger than this number, theywill be downsampled before fitting the propensity model. The modelwill be used to compute propensity scores for all subjects. Thepurpose of the sampling is to gain speed. Setting this number to 0means no downsampling will be applied.
If true, the function will test each covariate for correlation withthe treatment assignment. If any covariate has an unusually highcorrelation (either positive or negative), this will throw anderror.
If an error occurrs, should the function stop? Else, the two cohortswill be assumed to be perfectly separable.
The prior used to fit the model. SeecreatePrior for details.
The control object used to control the cross-validation used todetermine the hyperparameters of the prior (if applicable). SeecreateControl for details.
Create an object defining the parameter values.