Estimates a possible shift parameter for the data. See PDFModeler.estimateLeftShiftParameter(). If any of the data are negative then there will be no shift. There must be at least 3 different positive values for a shift to be estimated; otherwise, it will be 0.0. Any estimated shift that is less that the defaultZeroTolerance will be set to 0.0.

originalData

val originalData: DoubleArray

statistics

val statistics: StatisticIfc

Functions

bootStrapParameterEstimates

fun bootStrapParameterEstimates(result: EstimationResult, numBootstrapSamples: Int = 399, level: Double = 0.95, stream: RNStreamIfc = KSLRandom.nextRNStream()): List<BootstrapEstimate>

fun bootStrapParameterEstimates(estimator: MVBSEstimatorIfc, numBootstrapSamples: Int = 399, level: Double = 0.95, stream: RNStreamIfc = KSLRandom.nextRNStream(), label: String? = null): List<BootstrapEstimate>

Computes bootstrap confidence intervals for the estimated parameters of the distribution

confidenceIntervalForMinimum

fun confidenceIntervalForMinimum(numBootstrapSamples: Int = 399, level: Double = 0.95): Interval

Uses bootstrapping to estimate a confidence interval for the minimum

estimateAndEvaluateScores

fun estimateAndEvaluateScores(estimators: Set<ParameterEstimatorIfc> = allEstimators, automaticShifting: Boolean = true): PDFModelingResults

This function estimates the parameters based on the supplied estimators and scores the estimators based on the supplied scoring models scoringModels. By default, a shift parameter for the distributions is estimated. The results are bundles up into a class that holds the estimation results, the scoring results, and the model used for evaluating the model goodness of fit.

estimateParameters

fun estimateParameters(estimators: Set<ParameterEstimatorIfc>, automaticShifting: Boolean = true): List<EstimationResult>

Estimates the parameters for all estimators represented by the set of estimators. The parameter automaticShifting controls whether the data will be automatically shifted.

fun estimateParameters(estimator: ParameterEstimatorIfc, automaticShifting: Boolean = true): EstimationResult

A convenience method for invoking parameter estimation for a single instance of ParameterEstimatorIfc. The parameter automaticShifting controls whether the data will be automatically shifted.

evaluateScores

fun evaluateScores(estimationResults: List<EstimationResult>): PDFModelingResults

This function applies the supplied scoring models to the estimation results.

evaluateScoringResults

fun evaluateScoringResults(scoringResults: List<ScoringResult>, rankingMethod: Statistic.Companion.Ranking = defaultRankingMethod, model: AdditiveMODAModel = createDefaultPDFEvaluationModel(scoringResults, rankingMethod)): AdditiveMODAModel

Evaluates the supplied scoring results using the supplied evaluation model. A default additive MODA model is supplied that uses linear value functions for each metric.

htmlACFPlot

fun htmlACFPlot(plotFileName: String? = null): String

The ACF plot portion of the results as html. The optional argument plotFileName can be used to cause a PNG file to be saved to the plot directory.

htmlGoodnessOfFitSummary

fun htmlGoodnessOfFitSummary(pdfModelingResults: PDFModelingResults, evaluationMethod: EvaluationMethod = EvaluationMethod.Scoring, plotFileName: String? = null): String

Produces a html representation of the goodness of fit results which include the distribution fit quad plot and the chi-squared goodness of fit statistics.

htmlHistogram

fun htmlHistogram(plotFileName: String? = null): String

The histogram portion of the results as html. The optional argument plotFileName can be used to cause a PNG file to be saved to the plot directory.

htmlObservationPlot

fun htmlObservationPlot(plotFileName: String? = null): String

The observations plot portion of the results as html. The optional argument plotFileName can be used to cause a PNG file to be saved to the plot directory.

htmlScoringSummary

fun htmlScoringSummary(pdfModelingResults: PDFModelingResults, rankingMethod: Statistic.Companion.Ranking = defaultRankingMethod): String

Produces a html representation of the scoring and metric evaluation results including the recommended distribution.

htmlStatisticalSummary

fun htmlStatisticalSummary(): String

Presents a statistical summary of the data in html format. This includes that summary statistics, box plot statistics, histogram statistics, and an analysis of the shift parameter.

htmlVisualizationSummary

fun htmlVisualizationSummary(): String

Presents the histograms, box plot, observation plot, and auto-correlation plot for the data.

scoringResults

fun scoringResults(results: List<EstimationResult>): List<ScoringResult>

Estimation results in the list of results are scored by each scoring model in the supplied set scoring models. Any estimation results within the supplied list that were not successfully estimated or had no parameters estimated will not be scored. The returned list contains instances holding the scoring results for each successfully estimated distribution.

showAllResultsInBrowser

fun showAllResultsInBrowser(estimators: Set<ParameterEstimatorIfc> = allEstimators, automaticShifting: Boolean = true, pdfModelingResults: PDFModelingResults = estimateAndEvaluateScores(estimators, automaticShifting), rankingMethod: Statistic.Companion.Ranking = defaultRankingMethod, evaluationMethod: EvaluationMethod = EvaluationMethod.Scoring, statResultsFileName: String = "PDF_Modeling_Statistical_Summary", visualizationResultsFileName: String = "PDF_Modeling_Visualization_Summary", scoringResultsFileName: String = "PDF_Modeling_Scoring_Summary", goodnessOfFitResultsFileName: String = "PDF_Modeling_GoodnessOfFit_Summary")

This function will apply the estimators to the data and report all the results in HTML format.