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fix docs
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@egillax
egillax committed Apr 26, 2024
1 parent 62c5a21 commit 71954b9
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24 changes: 12 additions & 12 deletions R/DataSplitting.R
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#' 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).
#' default splitting functions (either random stratified by outcome, time or subject splitting)
#'
#' @details
#' Returns an object of class \code{splitSettings} that specifies the splitting function that will be called and the settings
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#' @param nfold (numeric) An integer > 1 specifying the number of folds used in cross validation
#' @param splitSeed (numeric) A seed to use when splitting the data for reproducibility (if not set a random number will be generated)
#' @param type (character) Choice of: \itemize{
#' \item{'stratified'}{ Each data point is randomly assigned into the test or a train fold set but this is done stratified such that the outcome rate is consistent in each partition }
#' \item{'time')}{ Older data are assigned into the training set and newer data are assigned into the test set}
#' \item{'subject'}{ Data are partitioned by subject, if a subject is in the data more than once, all the data points for the subject are assigned either into the test data or into the train data (not both).}
#' \item'stratified' Each data point is randomly assigned into the test or a train fold set but this is done stratified such that the outcome rate is consistent in each partition
#' \item'time' Older data are assigned into the training set and newer data are assigned into the test set
#' \item'subject' Data are partitioned by subject, if a subject is in the data more than once, all the data points for the subject are assigned either into the test data or into the train data (not both).
#' }
#'
#' @return
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#'
#' @details
#' Returns a list containing the training data (Train) and optionally the test data (Test). Train is an Andromeda object containing
#' \itemize{\item{covariates}{ a table (rowId, covariateId, covariateValue) containing the covariates for each data point in the train data }
#' \item{covariateRef}{ a table with the covariate information}
#' \item{labels)}{ a table (rowId, outcomeCount, ...) for each data point in the train data (outcomeCount is the class label) }
#' \item{folds}{ a table (rowId, index) specifying which training fold each data point is in.}
#' \itemize{\item covariates: a table (rowId, covariateId, covariateValue) containing the covariates for each data point in the train data
#' \item covariateRef: a table with the covariate information
#' \item labels: a table (rowId, outcomeCount, ...) for each data point in the train data (outcomeCount is the class label)
#' \item folds: a table (rowId, index) specifying which training fold each data point is in.
#' }
#' Test is an Andromeda object containing
#' \itemize{\item{covariates}{ a table (rowId, covariateId, covariateValue) containing the covariates for each data point in the test data }
#' \item{covariateRef}{ a table with the covariate information}
#' \item{labels)}{ a table (rowId, outcomeCount, ...) for each data point in the test data (outcomeCount is the class label) }
#' \itemize{\item covariates: a table (rowId, covariateId, covariateValue) containing the covariates for each data point in the test data
#' \item covariateRef: a table with the covariate information
#' \item labels: a table (rowId, outcomeCount, ...) for each data point in the test data (outcomeCount is the class label)
#' }
#'
#'
#'
#'
#' @param plpData An object of type \code{plpData} - the patient level prediction
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16 changes: 6 additions & 10 deletions R/DiagnosePlp.R
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Expand Up @@ -213,16 +213,12 @@ diagnoseMultiplePlp <- function(
#' and whether to normalise the covariates before training
#' @param modelSettings An object of class \code{modelSettings} created using one of the function:
#' \itemize{
#' \item{setLassoLogisticRegression()}{ A lasso logistic regression model}
#' \item{setGradientBoostingMachine()}{ A gradient boosting machine}
#' \item{setAdaBoost()}{ An ada boost model}
#' \item{setRandomForest()}{ A random forest model}
#' \item{setDecisionTree()}{ A decision tree model}
#' \item{setCovNN())}{ A convolutional neural network model}
#' \item{setCIReNN()}{ A recurrent neural network model}
#' \item{setMLP()}{ A neural network model}
#' \item{setDeepNN()}{ A deep neural network model}
#' \item{setKNN()}{ A KNN model}
#' \item setLassoLogisticRegression() A lasso logistic regression model
#' \item setGradientBoostingMachine() A gradient boosting machine
#' \item setAdaBoost() An ada boost model
#' \item setRandomForest() A random forest model
#' \item setDecisionTree() A decision tree model
#' \item setKNN() A KNN model
#'
#' }
#' @param logSettings An object of \code{logSettings} created using \code{createLogSettings}
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2 changes: 1 addition & 1 deletion R/FeatureEngineering.R
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Expand Up @@ -47,7 +47,7 @@ featureEngineer <- function(data, featureEngineeringSettings){
#' Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings
#'
#' @param type (character) Choice of: \itemize{
#' \item{'none'}{ No feature engineering - this is the default }
#' \item'none' No feature engineering - this is the default
#' }
#'
#' @return
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9 changes: 4 additions & 5 deletions R/Fit.R
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#' data extracted from the CDM.
#' @param modelSettings An object of class \code{modelSettings} created using one of the function:
#' \itemize{
#' \item{logisticRegressionModel()}{ A lasso logistic regression model}
#' \item{GBMclassifier()}{ A gradient boosting machine}
#' \item{RFclassifier()}{ A random forest model}
#' \item{GLMclassifier ()}{ A generalised linear model}
#' \item{KNNclassifier()}{ A KNN model}
#' \item setLassoLogisticRegression() A lasso logistic regression model
#' \item setGradientBoostingMachine() A gradient boosting machine
#' \item setRandomForest() A random forest model
#' \item setKNN() A KNN model
#' }
#' @param search The search strategy for the hyper-parameter selection (currently not used)
#' @param analysisId The id of the analysis
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19 changes: 7 additions & 12 deletions R/LearningCurve.R
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#' \code{trainFractions}. Note, providing \code{trainEvents} will override
#' your input to \code{trainFractions}. The format should be as follows:
#' \itemize{
#' \item{ \code{c(500, 1000, 1500) } - a list of training events}
#' \item \code{c(500, 1000, 1500) } - a list of training events
#' }
#' @param featureEngineeringSettings An object of \code{featureEngineeringSettings} specifying any feature engineering to be learned (using the train data)
#' @param preprocessSettings An object of \code{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
#' @param modelSettings An object of class \code{modelSettings} created using one of the function:
#' \itemize{
#' \item{setLassoLogisticRegression()}{ A lasso logistic regression model}
#' \item{setGradientBoostingMachine()}{ A gradient boosting machine}
#' \item{setAdaBoost()}{ An ada boost model}
#' \item{setRandomForest()}{ A random forest model}
#' \item{setDecisionTree()}{ A decision tree model}
#' \item{setCovNN())}{ A convolutional neural network model}
#' \item{setCIReNN()}{ A recurrent neural network model}
#' \item{setMLP()}{ A neural network model}
#' \item{setDeepNN()}{ A deep neural network model}
#' \item{setKNN()}{ A KNN model}
#'
#' \item \code{setLassoLogisticRegression()} A lasso logistic regression model
#' \item \code{setGradientBoostingMachine()} A gradient boosting machine
#' \item \code{setAdaBoost()} An ada boost model
#' \item \code{setRandomForest()} A random forest model
#' \item \code{setDecisionTree()} A decision tree model
#' \item \code{setKNN()} A KNN model
#' }
#' @param logSettings An object of \code{logSettings} created using \code{createLogSettings}
#' specifying how the logging is done
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12 changes: 6 additions & 6 deletions R/Logging.R
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#'
#' @param 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:
#' \itemize{
#' \item{DEBUG}{Highest verbosity showing all debug statements}
#' \item{TRACE}{Showing information about start and end of steps}
#' \item{INFO}{Show informative information (Default)}
#' \item{WARN}{Show warning messages}
#' \item{ERROR}{Show error messages}
#' \item{FATAL}{Be silent except for fatal errors}
#' \item DEBUG Highest verbosity showing all debug statements
#' \item TRACE Showing information about start and end of steps
#' \item INFO Show informative information (Default)
#' \item WARN Show warning messages
#' \item ERROR Show error messages
#' \item FATAL Be silent except for fatal errors
#' }
#' @param timeStamp If TRUE a timestamp will be added to each logging statement. Automatically switched on for TRACE level.
#' @param logName A string reference for the logger
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30 changes: 12 additions & 18 deletions R/RunPlp.R
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#' and whether to normalise the covariates before training
#' @param modelSettings An object of class \code{modelSettings} created using one of the function:
#' \itemize{
#' \item{setLassoLogisticRegression()}{ A lasso logistic regression model}
#' \item{setGradientBoostingMachine()}{ A gradient boosting machine}
#' \item{setAdaBoost()}{ An ada boost model}
#' \item{setRandomForest()}{ A random forest model}
#' \item{setDecisionTree()}{ A decision tree model}
#' \item{setCovNN())}{ A convolutional neural network model}
#' \item{setCIReNN()}{ A recurrent neural network model}
#' \item{setMLP()}{ A neural network model}
#' \item{setDeepNN()}{ A deep neural network model}
#' \item{setKNN()}{ A KNN model}
#'
#' \item setLassoLogisticRegression() A lasso logistic regression model
#' \item setGradientBoostingMachine() A gradient boosting machine
#' \item setAdaBoost() An ada boost model
#' \item setRandomForest() A random forest model
#' \item setDecisionTree() A decision tree model
#' \item setKNN() A KNN model
#' }
#' @param logSettings An object of \code{logSettings} created using \code{createLogSettings}
#' specifying how the logging is done
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#' An object containing the following:
#'
#' \itemize{
#' \item{inputSettings}{A list containing all the settings used to develop the model}
#' \item{model}{ The developed model of class \code{plpModel}}
#' \item{executionSummary}{ A list containing the hardward details, R package details and execution time}
#' \item{performanceEvaluation}{ Various internal performance metrics in sparse format}
#' \item{prediction}{ The plpData cohort table with the predicted risks added as a column (named value)}
#' \item{covariateSummary)}{ A characterization of the features for patients with and without the outcome during the time at risk}
#' \item{analysisRef}{ A list with details about the analysis}
#' \item model The developed model of class \code{plpModel}
#' \item executionSummary A list containing the hardward details, R package details and execution time
#' \item performanceEvaluation Various internal performance metrics in sparse format
#' \item prediction The plpData cohort table with the predicted risks added as a column (named value)
#' \item covariateSummary A characterization of the features for patients with and without the outcome during the time at risk
#' \item analysisRef A list with details about the analysis
#' }
#'
#'
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6 changes: 3 additions & 3 deletions R/Sampling.R
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#' Returns an object of class \code{sampleSettings} that specifies the sampling function that will be called and the settings
#'
#' @param type (character) Choice of: \itemize{
#' \item{'none'}{ No sampling is applied - this is the default }
#' \item{'underSample')}{Undersample the non-outcome class to make the data more ballanced}
#' \item{'overSample'}{Oversample the outcome class by adding in each outcome multiple times}
#' \item 'none' No sampling is applied - this is the default
#' \item 'underSample' Undersample the non-outcome class to make the data more ballanced
#' \item 'overSample' Oversample the outcome class by adding in each outcome multiple times
#' }
#' @param numberOutcomestoNonOutcomes (numeric) An numeric specifying the require number of non-outcomes per outcome
#' @param sampleSeed (numeric) A seed to use when splitting the data for reproducibility (if not set a random number will be generated)
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