createLearningCurve

Creates a learning curve object, which can be plotted using the plotLearningCurve() function.

createLearningCurve(
  plpData,
  outcomeId,
  parallel = T,
  cores = 4,
  modelSettings,
  saveDirectory = getwd(),
  analysisId = "learningCurve",
  populationSettings = createStudyPopulationSettings(),
  splitSettings = createDefaultSplitSetting(),
  trainFractions = c(0.25, 0.5, 0.75),
  trainEvents = NULL,
  sampleSettings = createSampleSettings(),
  featureEngineeringSettings = createFeatureEngineeringSettings(),
  preprocessSettings = createPreprocessSettings(minFraction = 0.001, normalize = T),
  logSettings = createLogSettings(),
  executeSettings = createExecuteSettings(runSplitData = T, runSampleData = F,
    runfeatureEngineering = F, runPreprocessData = T, runModelDevelopment = T,
    runCovariateSummary = F)
)

Arguments

plpData

An object of type plpData - the patient level prediction data extracted from the CDM.

outcomeId

(integer) The ID of the outcome.

parallel

Whether to run the code in parallel

cores

The number of computer cores to use if running in parallel

modelSettings

An object of class modelSettings created using one of the function:

• setLassoLogisticRegression() A lasso logistic regression model

• setGradientBoostingMachine() A gradient boosting machine

• setAdaBoost() An ada boost model

• setRandomForest() A random forest model

• setDecisionTree() A decision tree model

• setKNN() A KNN model

saveDirectory

The path to the directory where the results will be saved (if NULL uses working directory)

analysisId

(integer) Identifier for the analysis. It is used to create, e.g., the result folder. Default is a timestamp.

populationSettings

An object of type populationSettings created using createStudyPopulationSettings that specifies how the data class labels are defined and addition any exclusions to apply to the plpData cohort

splitSettings

An object of type splitSettings that specifies how to split the data into train/validation/test. The default settings can be created using createDefaultSplitSetting.

trainFractions

A list of training fractions to create models for. Note, providing trainEvents will override your input to trainFractions.

trainEvents

Events have shown to be determinant of model performance. Therefore, it is recommended to provide trainEvents rather than trainFractions. Note, providing trainEvents will override your input to trainFractions. The format should be as follows:

• c(500, 1000, 1500) - a list of training events

sampleSettings

An object of type sampleSettings that specifies any under/over sampling to be done. The default is none.

featureEngineeringSettings

An object of featureEngineeringSettings specifying any feature engineering to be learned (using the train data)

preprocessSettings

An object of preprocessSettings. This setting specifies the minimum fraction of target population who must have a covariate for it to be included in the model training and whether to normalise the covariates before training

logSettings

An object of logSettings created using createLogSettings specifying how the logging is done

executeSettings

An object of executeSettings specifying which parts of the analysis to run

Value

A learning curve object containing the various performance measures obtained by the model for each training set fraction. It can be plotted using plotLearningCurve.

Examples

if (FALSE) {
# define model
modelSettings = PatientLevelPrediction::setLassoLogisticRegression()

# create learning curve
learningCurve <- PatientLevelPrediction::createLearningCurve(population,
                                                             plpData,
                                                             modelSettings)
# plot learning curve
PatientLevelPrediction::plotLearningCurve(learningCurve)
}