Man pages for PatientLevelPrediction
Develop Clinical Prediction Models Using the Common Data Model
| averagePrecision | Calculate the average precision |
| brierScore | brierScore |
| calibrationInLarge | Calculate the calibration in large |
| calibrationLine | calibrationLine |
| computeAuc | Compute the area under the ROC curve |
| computeAuprc | Compute the area under the Precision-Recall curve |
| computeGridPerformance | Computes grid performance for a hyperparameter combination... |
| configurePython | Sets up a python environment to use for PLP (can be conda or... |
| covariateSummary | covariateSummary |
| createCohortCovariateSettings | Extracts covariates based on cohorts |
| createDatabaseDetails | Create a setting that holds the details about the cdmDatabase... |
| createDatabaseSchemaSettings | Create the PatientLevelPrediction database result schema... |
| createDefaultExecuteSettings | Creates default list of settings specifying what parts of... |
| createDefaultSplitSetting | Create the settings for defining how the plpData are split... |
| createExecuteSettings | Creates list of settings specifying what parts of runPlp to... |
| createExistingSplitSettings | Create the settings for defining how the plpData are split... |
| createFeatureEngineeringSettings | Create the settings for defining any feature engineering that... |
| createGlmModel | createGlmModel |
| createHyperparameterSettings | Create Hyperparameter Settings |
| createIterativeImputer | Create Iterative Imputer settings |
| createLearningCurve | createLearningCurve |
| createLogSettings | Create the settings for logging the progression of the... |
| createModelDesign | Specify settings for developing a single model |
| createNormalizer | Create the settings for normalizing the data @param type The... |
| createPlpResultTables | Create the results tables to store PatientLevelPrediction... |
| createPreprocessSettings | Create the settings for preprocessing the trainData. |
| createRandomForestFeatureSelection | Create the settings for random foreat based feature selection |
| createRareFeatureRemover | Create the settings for removing rare features |
| createRestrictPlpDataSettings | createRestrictPlpDataSettings define extra restriction... |
| createSampleSettings | Create the settings for defining how the trainData from... |
| createSimpleImputer | Create Simple Imputer settings |
| createSklearnIterativeImputer | Create scikit-learn Iterative Imputer settings |
| createSklearnModel | Plug an existing scikit learn python model into the PLP... |
| createSplineSettings | Create the settings for adding a spline for continuous... |
| createStratifiedImputationSettings | Create the settings for using stratified imputation. |
| createStudyPopulation | Create a study population |
| createStudyPopulationSettings | create the study population settings |
| createTempModelLoc | Create a temporary model location |
| createTuningMetric | Create a tuning metric descriptor |
| createUnivariateFeatureSelection | Create the settings for defining any feature selection that... |
| createValidationDesign | createValidationDesign - Define the validation design for... |
| createValidationSettings | createValidationSettings define optional settings for... |
| diagnoseMultiplePlp | Run a list of predictions diagnoses |
| diagnosePlp | diagnostic - Investigates the prediction problem settings -... |
| evaluatePlp | evaluatePlp |
| externalValidateDbPlp | externalValidateDbPlp - Validate a model on new databases |
| extractDatabaseToCsv | Exports all the results from a database into csv files |
| fitPlp | fitPlp |
| getCalibrationSummary | Get a sparse summary of the calibration |
| getCohortCovariateData | Extracts covariates based on cohorts |
| getDemographicSummary | Get a demographic summary |
| getEunomiaPlpData | Create a plpData object from the Eunomia database' |
| getPlpData | Extract the patient level prediction data from the server |
| getPredictionDistribution | Calculates the prediction distribution |
| getPredictionDistribution_binary | Calculates the prediction distribution |
| getThresholdSummary | Calculate all measures for sparse ROC |
| ici | Calculate the Integrated Calibration Index from Austin and... |
| insertCsvToDatabase | Function to insert results into a database from csvs |
| insertResultsToSqlite | Create sqlite database with the results |
| iterativeImpute | Imputation |
| listAppend | join two lists |
| listCartesian | Cartesian product |
| loadPlpAnalysesJson | Load the multiple prediction json settings from a file |
| loadPlpData | Load the plpData from a folder |
| loadPlpModel | loads the plp model |
| loadPlpResult | Loads the evalaution dataframe |
| loadPlpShareable | Loads the plp result saved as json/csv files for transparent... |
| loadPrediction | Loads the prediction dataframe to json |
| MapIds | Map covariate and row Ids so they start from 1 |
| migrateDataModel | Migrate Data model |
| minMaxNormalize | A function that normalizes continous features to have values... |
| modelBasedConcordance | Calculate the model-based concordance, which is a calculation... |
| outcomeSurvivalPlot | Plot the outcome incidence over time |
| PatientLevelPrediction | PatientLevelPrediction |
| pfi | Permutation Feature Importance |
| plotDemographicSummary | Plot the Observed vs. expected incidence, by age and gender |
| plotF1Measure | Plot the F1 measure efficiency frontier using the sparse... |
| plotGeneralizability | Plot the train/test generalizability diagnostic |
| plotLearningCurve | plotLearningCurve |
| plotNetBenefit | Plot the net benefit |
| plotPlp | Plot all the PatientLevelPrediction plots |
| plotPrecisionRecall | Plot the precision-recall curve using the sparse... |
| plotPredictedPDF | Plot the Predicted probability density function, showing... |
| plotPredictionDistribution | Plot the side-by-side boxplots of prediction distribution, by... |
| plotPreferencePDF | Plot the preference score probability density function,... |
| plotSmoothCalibration | Plot the smooth calibration as detailed in Calster et al. "A... |
| plotSparseCalibration | Plot the calibration |
| plotSparseCalibration2 | Plot the conventional calibration |
| plotSparseRoc | Plot the ROC curve using the sparse thresholdSummary data... |
| plotVariableScatterplot | Plot the variable importance scatterplot |
| pmmFit | Predictive mean matching using lasso |
| predictCyclops | Create predictive probabilities |
| predictGlm | predict using a logistic regression model |
| predictPlp | predictPlp |
| prepareHyperparameterGrid | Prepare Hyperparameter |
| preprocessData | A function that wraps around... |
| print.plpData | Print a plpData object |
| print.summary.plpData | Print a summary.plpData object |
| recalibratePlp | recalibratePlp |
| recalibratePlpRefit | recalibratePlpRefit |
| removeRareFeatures | A function that removes rare features from the data |
| robustNormalize | A function that normalizes continous by the interquartile... |
| runMultiplePlp | Run a list of predictions analyses |
| runPlp | runPlp - Develop and internally evaluate a model using... |
| savePlpAnalysesJson | Save the modelDesignList to a json file |
| savePlpData | Save the plpData to folder |
| savePlpModel | Saves the plp model |
| savePlpResult | Saves the result from runPlp into the location directory |
| savePlpShareable | Save the plp result as json files and csv files for... |
| savePrediction | Saves the prediction dataframe to a json file |
| setAdaBoost | Create setting for AdaBoost with python... |
| setCoxModel | Create setting for lasso Cox model |
| setDecisionTree | Create setting for the scikit-learn DecisionTree with python |
| setGradientBoostingMachine | Create setting for gradient boosting machine model using... |
| setIterativeHardThresholding | Create setting for Iterative Hard Thresholding model |
| setLassoLogisticRegression | Create modelSettings for lasso logistic regression |
| setLightGBM | Create setting for gradient boosting machine model using... |
| setMLP | Create setting for neural network model with python's... |
| setNaiveBayes | Create setting for naive bayes model with python |
| setPythonEnvironment | Use the python environment created using configurePython() |
| setRandomForest | Create setting for random forest model using sklearn |
| setRidgeRegression | Create modelSettings for ridge logistic regression |
| setSVM | Create setting for the python sklearn SVM (SVC function) |
| simpleImpute | Simple Imputation |
| simulatePlpData | Generate simulated data |
| simulationProfile | A simulation profile for generating synthetic patient level... |
| sklearnFromJson | Loads sklearn python model from json |
| sklearnToJson | Saves sklearn python model object to json in path |
| splitData | Split the plpData into test/train sets using a splitting... |
| summary.plpData | Summarize a plpData object |
| toSparseM | Convert the plpData in COO format into a sparse R matrix |
| validateExternal | validateExternal - Validate model performance on new data |
| validateMultiplePlp | externally validate the multiple plp models across new... |
| viewDatabaseResultPlp | open a local shiny app for viewing the result of a PLP... |
| viewMultiplePlp | open a local shiny app for viewing the result of a multiple... |
| viewPlp | viewPlp - Interactively view the performance and model... |
