I think the easy way to solve your question is to create a validation package using execute(createValidationPackage = T) in the codeToRun script.
To my knowledge,
In the “prediction.rds” file of plpResult folder, column “value” is the probability of the patient to have the outcome. (indexes -1 : test set, other indexes : training set)
In the “varImp.rds” file of the model folder, column “covariateValue” is variable importance of each covariate when you choose the model such as decision tree or gradient boosting (when regression, coefficient of the model),
also you can find “model.rds” file that have information about parameters.
If you want to test the model for the other dataset without validation plp package (that’s the same meaning with calculating the probability I think),
you should get a covariate data of the cohort from the database using like FeatureExtraction::getDbcovariateData and then you could calculate the probability using covariate data and model developed.
Please point out if there is something wrong and let me know if you find a better way. I’m always learning through forums.