applyModel: Apply train model on new data Apply a Patient Level...

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Apply train model on new data Apply a Patient Level Prediction model on Patient Level Prediction Data and get the predicted risk in [0,1] for each person in the population. If the user inputs a population with an outcomeCount column then the function also returns the evaluation of the prediction (AUC, brier score, calibration)

Usage

applyModel(
  population,
  plpData,
  plpModel,
  calculatePerformance = T,
  databaseOutput = NULL,
  silent = F
)

Arguments

population	The population of people who you want to predict the risk for
plpData	The plpData for the population
plpModel	The trained PatientLevelPrediction model
calculatePerformance	Whether to also calculate the performance metrics [default TRUE]
databaseOutput	Whether to save the details into the prediction database
silent	Whether to turn off progress reporting

Examples

## Not run: 
# load the model and data
plpData <- loadPlpData("C:/plpdata")
plpModel <- loadPlpModel("C:/plpmodel")

# use the same population settings as the model:
populationSettings <- plpModel$populationSettings
populationSettings$plpData <- plpData
population <- do.call(createStudyPopulation, populationSettings)

# get the prediction:
prediction <- applyModel(population, plpData, plpModel)$prediction

## End(Not run)

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.