Function reference • PatientLevelPrediction

Extracting data from the OMOP CDM database Functions for getting the necessary data from the database in Common Data Model and saving/loading.
`createDatabaseDetails()`	Create a setting that holds the details about the cdmDatabase connection for data extraction
`createRestrictPlpDataSettings()`	createRestrictPlpDataSettings define extra restriction settings when calling getPlpData
`getPlpData()`	Get the patient level prediction data from the server
`savePlpData()`	Save the cohort data to folder
`loadPlpData()`	Load the cohort data from a folder
`getCohortCovariateData()`	Extracts covariates based on cohorts
Settings for designing a prediction models Design settings required when developing a model.
`createStudyPopulationSettings()`	create the study population settings
`createDefaultSplitSetting()`	Create the settings for defining how the plpData are split into test/validation/train sets using default splitting functions (either random stratified by outcome, time or subject splitting)
`createSampleSettings()`	Create the settings for defining how the trainData from `splitData` are sampled using default sample functions.
`createFeatureEngineeringSettings()`	Create the settings for defining any feature engineering that will be done
`createPreprocessSettings()`	Create the settings for preprocessing the trainData.
Optional design settings Settings for optional steps that can be used in the PLP pipeline
`createCohortCovariateSettings()`	Extracts covariates based on cohorts
`createRandomForestFeatureSelection()`	Create the settings for random foreat based feature selection
`createUnivariateFeatureSelection()`	Create the settings for defining any feature selection that will be done
`createSplineSettings()`	Create the settings for adding a spline for continuous variables
`createStratifiedImputationSettings()`	Create the settings for adding a spline for continuous variables
External validation
`createValidationDesign()`	createValidationDesign - Define the validation design for external validation
`validateExternal()`	externalValidatePlp - Validate model performance on new data
`createValidationSettings()`	createValidationSettings define optional settings for performing external validation
`recalibratePlp()`	recalibratePlp
`recalibratePlpRefit()`	recalibratePlpRefit
Execution settings when developing a model Execution settings required when developing a model.
`createLogSettings()`	Create the settings for logging the progression of the analysis
`createExecuteSettings()`	Creates list of settings specifying what parts of runPlp to execute
`createDefaultExecuteSettings()`	Creates default list of settings specifying what parts of runPlp to execute
Binary Classification Models Functions for setting binary classifiers and their hyper-parameter search.
`setAdaBoost()`	Create setting for AdaBoost with python DecisionTreeClassifier base estimator
`setDecisionTree()`	Create setting for the scikit-learn 1.0.1 DecisionTree with python
`setGradientBoostingMachine()`	Create setting for gradient boosting machine model using gbm_xgboost implementation
`setKNN()`	Create setting for knn model
`setLassoLogisticRegression()`	Create setting for lasso logistic regression
`setMLP()`	Create setting for neural network model with python
`setNaiveBayes()`	Create setting for naive bayes model with python
`setRandomForest()`	Create setting for random forest model with python (very fast)
`setSVM()`	Create setting for the python sklearn SVM (SVC function)
`setIterativeHardThresholding()`	Create setting for lasso logistic regression
`setLightGBM()`	Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).
Survival Models Functions for setting survival models and their hyper-parameter search.
`setCoxModel()`	Create setting for lasso Cox model
Single Patient-Level Prediction Model Functions for training/evaluating/applying a single patient-level-prediction model
`runPlp()`	runPlp - Develop and internally evaluate a model using specified settings
`externalValidateDbPlp()`	externalValidateDbPlp - Validate a model on new databases
`savePlpModel()`	Saves the plp model
`loadPlpModel()`	loads the plp model
`savePlpResult()`	Saves the result from runPlp into the location directory
`loadPlpResult()`	Loads the evalaution dataframe
`diagnosePlp()`	diagnostic - Investigates the prediction problem settings - use before training a model
Multiple Patient-Level Prediction Models Functions for training mutliple patient-level-prediction model in an efficient way.
`createModelDesign()`	Specify settings for deceloping a single model
`runMultiplePlp()`	Run a list of predictions analyses
`validateMultiplePlp()`	externally validate the multiple plp models across new datasets
`savePlpAnalysesJson()`	Save the modelDesignList to a json file
`loadPlpAnalysesJson()`	Load the multiple prediction json settings from a file
`diagnoseMultiplePlp()`	Run a list of predictions diagnoses
Individual pipeline functions Functions for running parts of the PLP workflow
`createStudyPopulation()`	Create a study population
`splitData()`	Split the plpData into test/train sets using a splitting settings of class `splitSettings`
`preprocessData()`	A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features
`fitPlp()`	fitPlp
`predictPlp()`	predictPlp
`evaluatePlp()`	evaluatePlp
`covariateSummary()`	covariateSummary
Saving results into database Functions for saving the prediction model and performances into a database.
`insertResultsToSqlite()`	Create sqlite database with the results
`createPlpResultTables()`	Create the results tables to store PatientLevelPrediction models and results into a database
`addMultipleRunPlpToDatabase()`	Populate the PatientLevelPrediction results tables
`addRunPlpToDatabase()`	Function to add the run plp (development or validation) to database
`createDatabaseSchemaSettings()`	Create the PatientLevelPrediction database result schema settings
`createDatabaseList()`	Create a list with the database details and database meta data entries
`addDiagnosePlpToDatabase()`	Insert a diagnostic result into a PLP result schema database
`addMultipleDiagnosePlpToDatabase()`	Insert mutliple diagnosePlp results saved to a directory into a PLP result schema database
`extractDatabaseToCsv()`	Exports all the results from a database into csv files
`insertCsvToDatabase()`	Function to insert results into a database from csvs
`insertModelDesignInDatabase()`	Insert a model design into a PLP result schema database
`migrateDataModel()`	Migrate Data model
Shiny Viewers Functions for viewing results via a shiny app
`viewPlp()`	viewPlp - Interactively view the performance and model settings
`viewMultiplePlp()`	open a local shiny app for viewing the result of a multiple PLP analyses
`viewDatabaseResultPlp()`	open a local shiny app for viewing the result of a PLP analyses from a database
Plotting Functions for various performance plots
`plotPlp()`	Plot all the PatientLevelPrediction plots
`plotSparseRoc()`	Plot the ROC curve using the sparse thresholdSummary data frame
`plotSmoothCalibration()`	Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)
`plotSparseCalibration()`	Plot the calibration
`plotSparseCalibration2()`	Plot the conventional calibration
`plotDemographicSummary()`	Plot the Observed vs. expected incidence, by age and gender
`plotF1Measure()`	Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame
`plotGeneralizability()`	Plot the train/test generalizability diagnostic
`plotPrecisionRecall()`	Plot the precision-recall curve using the sparse thresholdSummary data frame
`plotPredictedPDF()`	Plot the Predicted probability density function, showing prediction overlap between true and false cases
`plotPreferencePDF()`	Plot the preference score probability density function, showing prediction overlap between true and false cases #'
`plotPredictionDistribution()`	Plot the side-by-side boxplots of prediction distribution, by class#'
`plotVariableScatterplot()`	Plot the variable importance scatterplot
`outcomeSurvivalPlot()`	Plot the outcome incidence over time
Learning Curves Functions for creating and plotting learning curves
`createLearningCurve()`	createLearningCurve
`plotLearningCurve()`	plotLearningCurve
Simulation Functions for simulating cohort method data objects.
`simulatePlpData()`	Generate simulated data
`plpDataSimulationProfile`	A simulation profile
Data manipulation functions Functions for manipulating data
`toSparseM()`	Convert the plpData in COO format into a sparse R matrix
`MapIds()`	Map covariate and row Ids so they start from 1
Helper/utility functions
`listAppend()`	join two lists
`listCartesian()`	Cartesian product
`createTempModelLoc()`	Create a temporary model location
`configurePython()`	Sets up a virtual environment to use for PLP (can be conda or python)
`setPythonEnvironment()`	Use the virtual environment created using configurePython()
Evaluation measures
`accuracy()`	Calculate the accuracy
`averagePrecision()`	Calculate the average precision
`brierScore()`	brierScore
`calibrationLine()`	calibrationLine
`computeAuc()`	Compute the area under the ROC curve
`f1Score()`	Calculate the f1Score
`falseDiscoveryRate()`	Calculate the falseDiscoveryRate
`falseNegativeRate()`	Calculate the falseNegativeRate
`falseOmissionRate()`	Calculate the falseOmissionRate
`falsePositiveRate()`	Calculate the falsePositiveRate
`ici()`	Calculate the Integrated Calibration Information from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281
`modelBasedConcordance()`	Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/
`negativeLikelihoodRatio()`	Calculate the negativeLikelihoodRatio
`negativePredictiveValue()`	Calculate the negativePredictiveValue
`positiveLikelihoodRatio()`	Calculate the positiveLikelihoodRatio
`positivePredictiveValue()`	Calculate the positivePredictiveValue
`sensitivity()`	Calculate the sensitivity
`specificity()`	Calculate the specificity
`computeGridPerformance()`	Computes grid performance with a specified performance function
`diagnosticOddsRatio()`	Calculate the diagnostic odds ratio
`getCalibrationSummary()`	Get a sparse summary of the calibration
`getDemographicSummary()`	Get a calibration per age/gender groups
`getThresholdSummary()`	Calculate all measures for sparse ROC
`getThresholdSummary_binary()`	Calculate all measures for sparse ROC when prediction is bianry classification
`getPredictionDistribution()`	Calculates the prediction distribution
`getPredictionDistribution_binary()`	Calculates the prediction distribution
Saving/loading models as json Functions for saving or loading models as json
`sklearnFromJson()`	Loads sklearn python model from json
`sklearnToJson()`	Saves sklearn python model object to json in path
Load/save for sharing Functions for loading/saving objects for sharing
`savePlpShareable()`	Save the plp result as json files and csv files for transparent sharing
`loadPlpShareable()`	Loads the plp result saved as json/csv files for transparent sharing
`loadPrediction()`	Loads the prediciton dataframe to csv
`savePrediction()`	Saves the prediction dataframe to RDS
Feature importance
`pfi()`	pfi
Other functions
`predictCyclops()`	Create predictive probabilities

Reference

Extracting data from the OMOP CDM database

Settings for designing a prediction models

Optional design settings

External validation

Execution settings when developing a model

Binary Classification Models

Survival Models

Single Patient-Level Prediction Model

Multiple Patient-Level Prediction Models

Individual pipeline functions

Saving results into database

Shiny Viewers

Plotting

Learning Curves

Simulation

Data manipulation functions

Helper/utility functions

Evaluation measures

Saving/loading models as json

Load/save for sharing

Feature importance

Other functions