createLearningCurvePar: createLearningCurvePar

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

Description

Creates a learning curve in parallel, which can be plotted using the plotLearningCurve() function. Currently this functionality is only supported by Lasso Logistic Regression.

Usage

createLearningCurvePar(
  population,
  plpData,
  modelSettings,
  testSplit = "stratified",
  testFraction = 0.25,
  trainFractions = c(0.25, 0.5, 0.75),
  trainEvents = NULL,
  splitSeed = NULL,
  nfold = 3,
  indexes = NULL,
  verbosity = "TRACE",
  minCovariateFraction = 0.001,
  normalizeData = T,
  saveDirectory = getwd(),
  savePlpData = F,
  savePlpResult = F,
  savePlpPlots = F,
  saveEvaluation = F,
  timeStamp = FALSE,
  analysisId = "lc-",
  cores = NULL
)

Arguments

population	The population created using createStudyPopulation() that will be used to develop the model.
plpData	An object of type plpData - the patient level prediction data extracted from the CDM.
modelSettings	An object of class modelSettings created using one of the function. Currently only one model is supported: setLassoLogisticRegression - a lasso logistic regression model
testSplit	Specifies the type of evaluation used. Can be either 'person' or 'time'. The value 'time' finds the date that splots the population into the testing and training fractions provided. Patients with an index after this date are assigned to the test set and patients with an index prior to this date are assigned to the training set. The value 'person' splits the data randomly into testing and training sets according to fractions provided. The split is stratified by the class label.
testFraction	The fraction of the data, which will be used as the testing set in the patient split evaluation.
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 #' @param splitSeed The seed used to split the testing and training set when using a 'person' type split
nfold	The number of folds used in the cross validation (default = 3).
indexes	A dataframe containing a rowId and index column where the index value of -1 means in the test set, and positive integer represents the cross validation fold (default is NULL).
verbosity	Sets the level of the verbosity. If the log level is at or higher in priority than the logger threshold, a message will print. The levels are: DEBUG - highest verbosity showing all debug statements TRACE - showing information about start and end of steps INFO - show informative messages (default) WARN - show warning messages ERROR - show error messages FATAL - be silent except for fatal errors
minCovariateFraction	Minimum covariate prevalence in population to avoid removal during preprocssing.
normalizeData	Whether to normalise the data
saveDirectory	Location to save log and results
savePlpData	Whether to save the plpData
savePlpResult	Whether to save the plpResult
savePlpPlots	Whether to save the plp plots
saveEvaluation	Whether to save the plp performance csv files
timeStamp	Include a timestamp in the log
analysisId	The analysis unique identifier
cores	The number of cores to use

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

## Not run: 
# define model
modelSettings = setLassoLogisticRegression()

# register parallel backend
registerParallelBackend()

# create learning curve
learningCurve <- createLearningCurvePar(population,
                                        plpData,
                                        modelSettings)
# plot learning curve
plotLearningCurve(learningCurve)

## End(Not run)

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