Uses of Interface
edu.cmu.tetrad.data.DataSet

Packages that use DataSet

Package	Description
edu.cmu.tetrad.algcomparison.algorithm.external
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calculator
edu.cmu.tetrad.calibration
edu.cmu.tetrad.classify
edu.cmu.tetrad.data
edu.cmu.tetrad.graph
edu.cmu.tetrad.regression
edu.cmu.tetrad.search	Contains classes for searching for (mostly structural) causal models given data.
edu.cmu.tetrad.search.score	Contains classes for various various sorts of scores for running score-based algorithms.
edu.cmu.tetrad.search.test	Contains classes for running conditional independence tests for various sorts of data.
edu.cmu.tetrad.search.utils	Contains some utility classes for search algorithms.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.simulation
edu.pitt.csb.mgm
edu.pitt.csb.stability
edu.pitt.dbmi.algo.bayesian.constraint.search
edu.pitt.dbmi.algo.resampling

Uses of DataSet in edu.cmu.tetrad.algcomparison.algorithm.external

Methods in edu.cmu.tetrad.algcomparison.algorithm.external with parameters of type DataSet

Modifier and Type	Method	Description
static Graph	ExternalAlgorithmPcalgPc.loadGraphPcAlgMatrix(DataSet dataSet)

Uses of DataSet in edu.cmu.tetrad.bayes

Methods in edu.cmu.tetrad.bayes that return DataSet

Modifier and Type	Method	Description
DataSet	ExtraCategoryInterpolator.filter(DataSet dataSet)
DataSet	ModeInterpolator.filter(DataSet dataSet)
DataSet	CellTableProbs.getDataSet()
DataSet	DataSetProbs.getDataSet()
DataSet	DirichletDataSetProbs.getDataSet()
DataSet	FactoredBayesStructuralEM.getDataSet()
DataSet	IntAveDataSetProbs.getDataSet()
DataSet	EmBayesEstimator.getMixedDataSet()
DataSet	BayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a sample with the given sample size.
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Overwrites the given dataSet with a new simulated dataSet, to avoid allocating memory.
DataSet	DirichletBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates and returns a dataset with number of cases equal to sampleSize.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Would be nice to have this method supported, but no one's using it, so it's not.
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved)
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved, int[] tiers)	Simulates a sample with the given sample size.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)
DataSet	MlBayesImObs.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a sample with the given sample size.
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)
DataSet	UpdatedBayesIm.simulateData(int sampleSize, boolean latentDataSaved)
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)

Methods in edu.cmu.tetrad.bayes with parameters of type DataSet

Modifier and Type	Method	Description
static DirichletBayesIm	DirichletEstimator.estimate(DirichletBayesIm prior, DataSet dataSet)
BayesIm	EmBayesProperties.Estimator.estimate(BayesPm bayesPm, DataSet dataSet)
BayesIm	MlBayesEstimator.estimate(BayesPm bayesPm, DataSet dataSet)	33 Estimates a Bayes IM using the variables, graph, and parameters in the given Bayes PM and the data columns in the given data set.
DataSet	ExtraCategoryInterpolator.filter(DataSet dataSet)
DataSet	ModeInterpolator.filter(DataSet dataSet)
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Overwrites the given dataSet with a new simulated dataSet, to avoid allocating memory.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Would be nice to have this method supported, but no one's using it, so it's not.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)

Constructors in edu.cmu.tetrad.bayes with parameters of type DataSet

Modifier	Constructor	Description
	BayesProperties(DataSet dataSet)
	BdeMetricCache(DataSet dataSet, BayesPm bayesPm)
	CellTableProbs(DataSet dataSet)	Creates a cell count table for the given data set.
	DataSetProbs(DataSet dataSet)	Creates a cell count table for the given data set.
	DirichletDataSetProbs(DataSet dataSet, double symmValue)	Creates a cell count table for the given data set.
	EmBayesEstimator(BayesIm inputBayesIm, DataSet dataSet)
	EmBayesEstimator(BayesPm bayesPm, DataSet dataSet)	Provides methods for estimating a Bayes IM from an existing BayesIM and a discrete dataset using EM (Expectation Maximization).
	EmBayesProperties(DataSet dataSet, Graph graph)
	FactoredBayesStructuralEM(DataSet dataSet, BayesPm bayesPmM0)
	IntAveDataSetProbs(DataSet dataSet)	Creates a cell count table for the given data set.

Uses of DataSet in edu.cmu.tetrad.calculator

Methods in edu.cmu.tetrad.calculator with parameters of type DataSet

Modifier and Type	Method	Description
static void	Transformation.transform(DataSet data, String... equations)	Transforms the given data using the given representations of transforming equations.

Uses of DataSet in edu.cmu.tetrad.calibration

Methods in edu.cmu.tetrad.calibration that return DataSet

Modifier and Type	Method	Description
DataSet	DataForCalibrationRfci.bootStrapSampling(DataSet data, int bootsrapSampleSize)

Methods in edu.cmu.tetrad.calibration with parameters of type DataSet

Modifier and Type	Method	Description
DataSet	DataForCalibrationRfci.bootStrapSampling(DataSet data, int bootsrapSampleSize)
Graph	DataForCalibrationRfci.learnBNRFCI(DataSet bootstrapSample, int depth, Graph truePag)

Uses of DataSet in edu.cmu.tetrad.classify

Methods in edu.cmu.tetrad.classify that return DataSet

Modifier and Type	Method	Description
DataSet	ClassifierBayesUpdaterDiscrete.getTestData()	Returns the test data being used.

Constructors in edu.cmu.tetrad.classify with parameters of type DataSet

Modifier	Constructor	Description
	ClassifierBayesUpdaterDiscrete(BayesIm bayesIm, DataSet testData)

Uses of DataSet in edu.cmu.tetrad.data

Classes in edu.cmu.tetrad.data that implement DataSet

Modifier and Type	Class	Description
final class	BoxDataSet	Wraps a DataBox in such a way that mixed data sets can be stored.
final class	NumberObjectDataSet	Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.

Methods in edu.cmu.tetrad.data that return DataSet

Modifier and Type	Method	Description
static DataSet	DataTransforms.addMissingData(DataSet inData, double[] probs)	Adds missing data values to cases in accordance with probabilities specified in a double array which has as many elements as there are columns in the input dataset.
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
static DataSet	DataUtils.choleskySimulation(CovarianceMatrix cov)
static DataSet	DataTransforms.concatenate(DataSet... dataSets)
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)
DataSet	BoxDataSet.copy()
DataSet	DataSet.copy()
DataSet	NumberObjectDataSet.copy()
static DataSet	DataUtils.discreteSerializableInstance()	A discrete data set used to construct some other serializable instances.
static DataSet	DataTransforms.discretize(DataSet dataSet, int numCategories, boolean variablesCopied)
DataSet	Discretizer.discretize()
DataSet	CaseExpander.filter(DataSet dataSet)	Expands cases for the given dataset.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled with replacement.
static DataSet	SimpleDataLoader.getContinuousDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is continuous.
DataSet	Histogram.getDataSet()
static DataSet	SimpleDataLoader.getDiscreteDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is discrete.
static DataSet	SimpleDataLoader.getMixedDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is mixed.
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled without replacement.
DataSet	BoxDataSet.like()
DataSet	DataSet.like()
DataSet	NumberObjectDataSet.like()
static @NotNull DataSet	SimpleDataLoader.loadContinuousData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, edu.pitt.dbmi.data.reader.Delimiter delimiter, boolean excludeFirstColumn)	Loads a continuous dataset from a file.
static @NotNull DataSet	SimpleDataLoader.loadDiscreteData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, edu.pitt.dbmi.data.reader.Delimiter delimiter, boolean excludeFirstColumn)	Loads a discrete dataset from a file.
static @NotNull DataSet	SimpleDataLoader.loadMixedData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, int maxNumCategories, edu.pitt.dbmi.data.reader.Delimiter delimiter, boolean excludeFirstColumn)	Loads a mixed dataset from a file.
static DataSet	DataTransforms.logData(DataSet dataSet, double a, boolean isUnlog, int base)	Log or unlog data
static DataSet	DataTransforms.removeConstantColumns(DataSet dataSet)
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)
DataSet	BootstrapSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	RandomSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)
DataSet	Simulator.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
static DataSet	DataTransforms.standardizeData(DataSet dataSet)
DataSet	BoxDataSet.subsetColumns(int[] indices)
DataSet	BoxDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	DataSet.subsetColumns(int[] columns)
DataSet	DataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	NumberObjectDataSet.subsetColumns(int[] indices)
DataSet	NumberObjectDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	BoxDataSet.subsetRows(int[] rows)
DataSet	DataSet.subsetRows(int[] rows)
DataSet	NumberObjectDataSet.subsetRows(int[] rows)
DataSet	BoxDataSet.subsetRowsColumns(int[] rows, int[] columns)
DataSet	DataSet.subsetRowsColumns(int[] rows, int[] columns)
DataSet	NumberObjectDataSet.subsetRowsColumns(int[] rows, int[] columns)

Methods in edu.cmu.tetrad.data that return types with arguments of type DataSet

Modifier and Type	Method	Description
static List<DataSet>	DataTransforms.center(List<DataSet> dataList)
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)

Methods in edu.cmu.tetrad.data with parameters of type DataSet

Modifier and Type	Method	Description
static DataSet	DataTransforms.addMissingData(DataSet inData, double[] probs)	Adds missing data values to cases in accordance with probabilities specified in a double array which has as many elements as there are columns in the input dataset.
void	CellTable.addToTable(DataSet dataSet, int[] indices)
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
static DataSet	DataTransforms.concatenate(DataSet... dataSets)
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)
static boolean	DataUtils.containsMissingValue(DataSet data)
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)
static void	DataTransforms.copyColumn(Node node, DataSet source, DataSet dest)
static ICovarianceMatrix	DataTransforms.covarianceNonparanormalDrton(DataSet dataSet)
static DataSet	DataTransforms.discretize(DataSet dataSet, int numCategories, boolean variablesCopied)
DataSet	CaseExpander.filter(DataSet dataSet)	Expands cases for the given dataset.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled with replacement.
static List<Node>	DataTransforms.getConstantColumns(DataSet dataSet)
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCorrelationMatrix(DataSet dataSet)
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCovarianceMatrix(DataSet dataSet, boolean precomputeCovariances)
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled without replacement.
static boolean	DataUtils.isBinary(DataSet data, int column)	States whether the given column of the given data set is binary.
static DataSet	DataTransforms.logData(DataSet dataSet, double a, boolean isUnlog, int base)	Log or unlog data
static DataSet	DataTransforms.removeConstantColumns(DataSet dataSet)
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)
DataSet	BootstrapSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	RandomSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)
static DataSet	DataTransforms.standardizeData(DataSet dataSet)
static void	DataWriter.writeRectangularData(DataSet dataSet, Writer out, char separator)	Writes a dataset to file.

Method parameters in edu.cmu.tetrad.data with type arguments of type DataSet

Modifier and Type	Method	Description
static List<DataSet>	DataTransforms.center(List<DataSet> dataList)
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)

Constructors in edu.cmu.tetrad.data with parameters of type DataSet

Modifier	Constructor	Description
	CorrelationMatrix(DataSet dataSet)	Constructs a new correlation matrix from the the given DataSet.
	CovarianceMatrix(DataSet dataSet)	Constructs a new covariance matrix from the given data set.
	CovarianceMatrix(DataSet dataSet, boolean biasCorrected)
	CovarianceMatrixOnTheFly(DataSet dataSet)	Constructs a new covariance matrix from the given data set.
	CovarianceMatrixOnTheFly(DataSet dataSet, boolean verbose)
	Discretizer(DataSet dataSet)	Constructs a new discretizer that discretizes every variable as binary, using evenly distributed values.
	Discretizer(DataSet dataSet, Map<Node,DiscretizationSpec> specs)
	Histogram(DataSet dataSet, String target, boolean removeZeroPointsPerPlot)	This histogram is for variables in a particular data set.

Uses of DataSet in edu.cmu.tetrad.graph

Methods in edu.cmu.tetrad.graph with parameters of type DataSet

Modifier and Type	Method	Description
static Graph	GraphSaveLoadUtils.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet)

Uses of DataSet in edu.cmu.tetrad.regression

Methods in edu.cmu.tetrad.regression that return DataSet

Modifier and Type	Method	Description
static DataSet	RegressionUtils.residuals(DataSet dataSet, Graph graph)

Methods in edu.cmu.tetrad.regression with parameters of type DataSet

Modifier and Type	Method	Description
static DataSet	RegressionUtils.residuals(DataSet dataSet, Graph graph)

Constructors in edu.cmu.tetrad.regression with parameters of type DataSet

Modifier	Constructor	Description
	LogisticRegression(DataSet dataSet)	A mixed data set.
	RegressionDataset(DataSet data)	Constructs a linear regression model for the given tabular data set.

Uses of DataSet in edu.cmu.tetrad.search

Methods in edu.cmu.tetrad.search that return DataSet

Modifier and Type	Method	Description
DataSet	IndTestIod.getData()	The data set for this test is undefined, since the test is pooled over multiple datasets.

Methods in edu.cmu.tetrad.search that return types with arguments of type DataSet

Modifier and Type	Method	Description
default List<DataSet>	IndependenceTest.getDataSets()	Returns the datasets for this test

Methods in edu.cmu.tetrad.search with parameters of type DataSet

Modifier and Type	Method	Description
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA)	Estimates the W matrix using FastICA.
Matrix	IcaLingam.fit(DataSet D)	Fits an ICA-LiNGAM model to the given dataset using a default method for estimating W.
List<Matrix>	IcaLingD.fit(DataSet D)	Fits a LiNG-D model to the given dataset using a default method for estimating W.
LinkedList<LinkedList<Cstar.Record>>	Cstar.getRecords(DataSet dataSet, List<Node> possibleCauses, List<Node> possibleEffects, int topBracket, String path)	Returns records for a set of variables with expected number of false positives bounded by q.

Constructors in edu.cmu.tetrad.search with parameters of type DataSet

Modifier	Constructor	Description
	Bpc(DataSet dataSet, double alpha, BpcTestType sigTestType)	Constructor.
	Dagma(DataSet dataset)	Constructor.
	DirectLingam(DataSet dataset, Score score)	Constructor.
	FactorAnalysis(DataSet dataSet)	Constructor.
	Fask(DataSet dataSet, Score score, IndependenceTest test)	Constructor.
	Fofc(DataSet dataSet, BpcTestType testType, Fofc.Algorithm algorithm, double alpha)	Conctructor.
	Ftfc(DataSet dataSet, Ftfc.Algorithm algorithm, double alpha)	Conctructor.
	Ida(DataSet dataSet, Graph cpdag, List<Node> possibleCauses)	Constructor.
	PcLingam(Graph cpdag, DataSet dataSet)	Constructor.

Constructor parameters in edu.cmu.tetrad.search with type arguments of type DataSet

Modifier	Constructor	Description
	Lofs(Graph graph, List<DataSet> dataSets)	Constructor.

Uses of DataSet in edu.cmu.tetrad.search.score

Methods in edu.cmu.tetrad.search.score that return DataSet

Modifier and Type	Method	Description
DataSet	BdeScore.getDataSet()	Returns the dataset being analyzed.
DataSet	BdeuScore.getDataSet()	Returns the dataset being analyzed.
DataSet	DiscreteBicScore.getDataSet()	Returns the dataset being analyzed.
DataSet	DiscreteScore.getDataSet()	Returns the dataset.
DataSet	GicScores.getDataSet()
DataSet	GraphScore.getDataSet()
DataSet	IndTestScore.getDataSet()

Constructors in edu.cmu.tetrad.search.score with parameters of type DataSet

Modifier	Constructor	Description
	BdeScore(DataSet dataSet)	Constructs a BDe score for the given dataset.
	BdeuScore(DataSet dataSet)	Constructs a BDe score for the given dataset.
	ConditionalGaussianLikelihood(DataSet dataSet)	Constructs the score using a covariance matrix.
	ConditionalGaussianScore(DataSet dataSet, double penaltyDiscount, boolean discretize)	Constructs the score using a covariance matrix.
	DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances)
	DiscreteBicScore(DataSet dataSet)	Constructor.
	EbicScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	GicScores(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	MvpLikelihood(DataSet dataSet, double structurePrior, int fDegree, boolean discretize)
	MvpScore(DataSet dataSet, double structurePrior, int fDegree, boolean discretize)	Constructor.
	PoissonPriorScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	SemBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	ZsbScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.

Uses of DataSet in edu.cmu.tetrad.search.test

Methods in edu.cmu.tetrad.search.test that return DataSet

Modifier and Type	Method	Description
DataSet	IndTestChiSquare.getData()	Returns the data being analyzed.
DataSet	IndTestConditionalCorrelation.getData()	Returns the data set being analyzed.
DataSet	IndTestConditionalGaussianLrt.getData()	Returns the data.
DataSet	IndTestDegenerateGaussianLrt.getData()	Returns the dataset being analyzed.
DataSet	IndTestFisherZ.getData()	Returns the data set being analyzed.
DataSet	IndTestFisherZConcatenateResiduals.getData()	Returns the concatenated data.
DataSet	IndTestFisherZFisherPValue.getData()	Returns the concatenated data.
DataSet	IndTestGSquare.getData()	Returns the data.
DataSet	IndTestHsic.getData()	Returns the data set being analyzed.
DataSet	IndTestMulti.getData()
DataSet	IndTestMvpLrt.getData()	Returns the data.
DataSet	IndTestRegression.getData()
DataSet	IndTestTrekSep.getData()
DataSet	MsepTest.getData()
DataSet	GSquareTest.getDataSet()	Returns the dataset used for this test.

Methods in edu.cmu.tetrad.search.test that return types with arguments of type DataSet

Modifier and Type	Method	Description
List<DataSet>	IndTestFisherZ.getDataSets()	Returns the (singleton) list of datasets being analyzed.
List<DataSet>	IndTestTrekSep.getDataSets()
List<DataSet>	Kci.getDataSets()	Returns a list consisting of the dataset for this test.
List<DataSet>	ScoreIndTest.getDataSets()

Constructors in edu.cmu.tetrad.search.test with parameters of type DataSet

Modifier	Constructor	Description
	ChiSquareTest(DataSet dataSet, double alpha)	Constructs a test using the given data set and significance level.
	ConditionalCorrelationIndependence(DataSet dataSet)	Constructs a new Independence test which checks independence facts based on the correlation data implied by the given data set (must be continuous).
	GSquareTest(DataSet dataSet, double alpha)	Constructor
	IndTestChiSquare(DataSet dataSet, double alpha)	Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
	IndTestConditionalCorrelation(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation data implied by the given data set (must be continuous).
	IndTestConditionalGaussianLrt(DataSet data, double alpha, boolean discretize)	Constructor.
	IndTestDegenerateGaussianLrt(DataSet dataSet)	Constructs the score using a covariance matrix.
	IndTestFisherZ(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestGSquare(DataSet dataSet, double alpha)	Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
	IndTestHsic(DataSet dataSet, double alpha)	Constructs a new HSIC Independence test.
	IndTestMvpLrt(DataSet data, double alpha, int fDegree, boolean discretize)	Constructor.
	IndTestProbabilistic(DataSet dataSet)	Initializes the test using a discrete data sets.
	IndTestRegression(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	Kci(DataSet data, double alpha)	Constructor.

Constructor parameters in edu.cmu.tetrad.search.test with type arguments of type DataSet

Modifier	Constructor	Description
	IndTestFisherZConcatenateResiduals(List<DataSet> dataSets, double alpha)	Constructor.
	IndTestFisherZFisherPValue(List<DataSet> dataSets, double alpha)	Constructor.

Uses of DataSet in edu.cmu.tetrad.search.utils

Methods in edu.cmu.tetrad.search.utils that return DataSet

Modifier and Type	Method	Description
static DataSet	TsUtils.addIndex(DataSet data)	Creates new time series dataset from the given one with index variable (e.g., time)
static DataSet	TsUtils.ar(DataSet timeSeries, int numLags)
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)
static DataSet	TsUtils.createLagData(DataSet data, int numLags)	Creates new time series dataset from the given one (fixed to deal with mixed datasets)
static DataSet	TsUtils.createShiftedData(DataSet data, int[] shifts)
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
DataSet	TetradTest.getDataSet()
DataSet	TetradTestContinuous.getDataSet()
DataSet	TetradTestDiscrete.getDataSet()
DataSet	TetradTestPopulation.getDataSet()
DataSet	TsUtils.VarResult.getResiduals()

Methods in edu.cmu.tetrad.search.utils with parameters of type DataSet

Modifier and Type	Method	Description
static DataSet	TsUtils.addIndex(DataSet data)	Creates new time series dataset from the given one with index variable (e.g., time)
static DataSet	TsUtils.ar(DataSet timeSeries, int numLags)
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)
static Matrix	KernelUtils.constructCentralizedGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs the centralized Gram matrix for a given vector valued sample.
static Matrix	KernelUtils.constructGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs Gram matrix for a given vector valued sample.
static DataSet	TsUtils.createLagData(DataSet data, int numLags)	Creates new time series dataset from the given one (fixed to deal with mixed datasets)
static DataSet	TsUtils.createShiftedData(DataSet data, int[] shifts)
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
static double[]	TsUtils.getSelfLoopCoefs(DataSet timeSeries)
static Matrix	KernelUtils.incompleteCholeskyGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes, double precision)	Approximates Gram matrix using incomplete Cholesky factorization
void	Kernel.setDefaultBw(DataSet dataset, Node node)	Sets bandwidth from data using default method
void	KernelGaussian.setDefaultBw(DataSet dataset, Node node)	Default setting of bandwidth based on median distance heuristic
void	KernelGaussian.setMedianBandwidth(DataSet dataset, Node node)	Sets the bandwidth of the kernel to median distance between two points in the given vector
static TsUtils.VarResult	TsUtils.structuralVar(DataSet timeSeries, int numLags)
static double	TsUtils.sumOfArCoefficients(DataSet timeSeries, int numLags)

Constructors in edu.cmu.tetrad.search.utils with parameters of type DataSet

Modifier	Constructor	Description
	AdLeafTree(DataSet dataSet)	Constructs an AD Leaf Tree for the given dataset.
	DeltaSextadTest(DataSet dataSet)	Constructs a test using a given data set.
	DeltaTetradTest(DataSet dataSet)	Constructs a test using a given data set.
	FgesOrienter(DataSet dataSet)	The data set must either be all continuous or all discrete.
	KernelGaussian(DataSet dataset, Node node)	Creates a new Gaussian kernel using the median distance between points to set the bandwidth
	Purify(DataSet dataSet, double sig, BpcTestType testType, Clusters clusters)
	TetradTestContinuous(DataSet dataSet, BpcTestType sigTestType, double sig)
	TetradTestDiscrete(DataSet dataSet, double sig)
	VarResult(DataSet dataSet, Graph collapsedVarGraph)

Uses of DataSet in edu.cmu.tetrad.search.work_in_progress

Methods in edu.cmu.tetrad.search.work_in_progress that return DataSet

Modifier and Type	Method	Description
DataSet	DMSearch.getData()
DataSet	IndTestCramerT.getData()
DataSet	IndTestFisherZGeneralizedInverse.getData()	Returns the data being analyzed.
DataSet	IndTestFisherZPercentIndependent.getData()
DataSet	IndTestFisherZRecursive.getData()
DataSet	IndTestMixedMultipleTTest.getData()	Returne the original data for the method.
DataSet	IndTestMnlrLr.getData()	Returns the data.
DataSet	IndTestMultinomialLogisticRegression.getData()
DataSet	IndTestPositiveCorr.getData()
DataSet	IndTestSepsetDci.getData()
DataSet	SemBicScoreDeterministic.getDataSet()

Methods in edu.cmu.tetrad.search.work_in_progress that return types with arguments of type DataSet

Modifier and Type	Method	Description
List<DataSet>	IndTestFisherZPercentIndependent.getDataSets()
List<DataSet>	IndTestFisherZRecursive.getDataSets()
List<DataSet>	IndTestPositiveCorr.getDataSets()

Methods in edu.cmu.tetrad.search.work_in_progress with parameters of type DataSet

Modifier and Type	Method	Description
void	DMSearch.setData(DataSet data)

Constructors in edu.cmu.tetrad.search.work_in_progress with parameters of type DataSet

Modifier	Constructor	Description
	BpcTetradPurifyWashdown(DataSet dataSet, BpcTestType testType, double alpha)	Construct the algorithm using a data set.
	FasLofs(DataSet dataSet, Lofs.Rule rule)
	HbsmsBeam(Graph graph, DataSet data, Knowledge knowledge)
	HbsmsGes(Graph graph, DataSet data)
	IndTestCramerT(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestFisherZGeneralizedInverse(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestFisherZRecursive(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestMixedMultipleTTest(DataSet data, double alpha)
	IndTestMnlrLr(DataSet data, double alpha)
	IndTestMultinomialLogisticRegression(DataSet data, double alpha)
	IndTestPositiveCorr(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestUniformScatter(DataSet dataSet, double alpha, double avgCountPerCell, int numCondCategories)
	InverseCorrelation(DataSet dataSet, double threshold)
	Kpc(DataSet dataset, double alpha)	Constructs a new PC search using for the given dataset.
	MagSemBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	Mmhc(IndependenceTest test, DataSet dataSet)
	MnlrLikelihood(DataSet dataSet, double structurePrior, int fDegree)	Constructor.
	MnlrScore(DataSet dataSet, double structurePrior, int fDegree)	Constructor.
	ProbabilisticMapIndependence(DataSet dataSet)	Initializes the test using a discrete data sets.
	Washdown(DataSet data, double alpha)	Constructor.

Constructor parameters in edu.cmu.tetrad.search.work_in_progress with type arguments of type DataSet

Modifier	Constructor	Description
	FaskVote(List<DataSet> dataSets, ScoreWrapper score, IndependenceWrapper test)	Constructor.
	IndTestFisherZPercentIndependent(List<DataSet> dataSets, double alpha)

Uses of DataSet in edu.cmu.tetrad.sem

Methods in edu.cmu.tetrad.sem that return DataSet

Modifier and Type	Method	Description
DataSet	DagScorer.getDataSet()
DataSet	Scorer.getDataSet()
DataSet	SemEstimator.getDataSet()
DataSet	GeneralizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)
DataSet	SemIm.simulateData(int sampleSize, boolean latentDataSaved)	This simulate method uses the implied covariance metrix directly to simulate data, instead of going tier by tier.
DataSet	StandardizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)	A convenience method, in case we want to change out mind about how to simulate.
DataSet	GeneralizedSemIm.simulateDataAvoidInfinity(int sampleSize, boolean latentDataSaved)
DataSet	SemIm.simulateDataCholesky(int sampleSize, boolean latentDataSaved)	Simulates data from this Sem using a Cholesky decomposition of the implied covariance matrix.
DataSet	GeneralizedSemIm.simulateDataFisher(int sampleSize)	Simulates data using the model of R.
DataSet	GeneralizedSemIm.simulateDataFisher(int sampleSize, int intervalBetweenShocks, double epsilon)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(double[][] shocks, int intervalBetweenShocks, double epsilon)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(int sampleSize)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(int intervalBetweenShocks, int intervalBetweenRecordings, int sampleSize, double epsilon, boolean saveLatentVars)
DataSet	GeneralizedSemIm.simulateDataMinimizeSurface(int sampleSize, boolean latentDataSaved)
DataSet	GeneralizedSemIm.simulateDataNSteps(int sampleSize, boolean latentDataSaved)
DataSet	GeneralizedSemIm.simulateDataRecursive(int sampleSize, boolean latentDataSaved)	This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
DataSet	LargeScaleSimulation.simulateDataRecursive(int sampleSize)	This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
DataSet	SemIm.simulateDataRecursive(int sampleSize, boolean latentDataSaved)
DataSet	LargeScaleSimulation.simulateDataReducedForm(int sampleSize)	Simulates data using the model X = (I - B)Y^-1 * e.
DataSet	SemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)
DataSet	StandardizedSemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)

Methods in edu.cmu.tetrad.sem with parameters of type DataSet

Modifier and Type	Method	Description
GeneralizedSemIm	GeneralizedSemEstimator.estimate(GeneralizedSemPm pm, DataSet data)	Maximizes likelihood equation by equation.
static List<String>	ReidentifyVariables.reidentifyVariables2(List<List<Node>> clusters, Graph trueGraph, DataSet data)
void	SemIm.setDataSet(DataSet dataSet)	Calculates the covariance matrix of the given DataSet and sets the sample covariance matrix for this model to a subset of it.

Constructors in edu.cmu.tetrad.sem with parameters of type DataSet

Modifier	Constructor	Description
	DagScorer(DataSet dataSet)	Constructs a new SemEstimator that uses the specified optimizer.
	SemEstimator(DataSet dataSet, SemPm semPm)	Constructs a Sem Estimator that does default estimation.
	SemEstimator(DataSet dataSet, SemPm semPm, SemOptimizer semOptimizer)	Constructs a new SemEstimator that uses the specified optimizer.

Uses of DataSet in edu.cmu.tetrad.simulation

Methods in edu.cmu.tetrad.simulation that return DataSet

Modifier and Type	Method	Description
DataSet	Hsim.hybridsimulate()
DataSet	HsimContinuous.hybridsimulate()

Methods in edu.cmu.tetrad.simulation with parameters of type DataSet

Modifier and Type	Method	Description
static VerticalIntDataBox	HsimUtils.makeVertIntBox(DataSet dataset)
void	GdistanceRandom.setLocationMap(DataSet map)

Constructors in edu.cmu.tetrad.simulation with parameters of type DataSet

Modifier	Constructor	Description
	Gdistance(DataSet locationMap, double xDist, double yDist, double zDist)
	GdistanceRandom(DataSet inMap)
	Hsim(Dag thedag, Set<Node> thesimnodes, DataSet thedata)
	HsimAutoC(DataSet indata)
	HsimAutoRun(DataSet indata)
	HsimContinuous(Dag thedag, Set<Node> thesimnodes, DataSet thedata)
	HsimRepeatAC(DataSet indata)
	HsimRepeatAutoRun(DataSet indata)
	Vicinity(List<Edge> edges, DataSet locationMap, int xLow, int xHigh, int yLow, int yHigh, int zLow, int zHigh, double xDist, double yDist, double zDist)

Uses of DataSet in edu.pitt.csb.mgm

Methods in edu.pitt.csb.mgm that return DataSet

Modifier and Type	Method	Description
static DataSet	MixedUtils.deepCopy(DataSet ds)	Makes a deep copy of a dataset (Nodes copied as well).
static DataSet	MixedUtils.getContinousData(DataSet ds)
DataSet	IndTestMultinomialLogisticRegressionWald.getData()
static DataSet	MixedUtils.getDiscreteData(DataSet ds)
static DataSet	MixedUtils.loadData(String dir, String filename)
static DataSet	MixedUtils.loadDataSet(String dir, String filename)
static DataSet	MixedUtils.loadDelim(String dir, String filename)
static DataSet	MixedUtils.makeContinuousData(DataSet dsMix)
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)

Methods in edu.pitt.csb.mgm with parameters of type DataSet

Modifier and Type	Method	Description
static DataSet	MixedUtils.deepCopy(DataSet ds)	Makes a deep copy of a dataset (Nodes copied as well).
static DataSet	MixedUtils.getContinousData(DataSet ds)
static int[]	MixedUtils.getDiscLevels(DataSet ds)
static DataSet	MixedUtils.getDiscreteData(DataSet ds)
static IndependenceTest	MixedUtils.IndTestFromString(String name, DataSet data, double alpha)	Returns independence tests by name located in edu.cmu.tetrad.search and edu.pitt.csb.mgm also supports shorthand for LRT ("lrt) and t-tests ("tlin" for prefer linear (fastest) or "tlog" for prefer logistic)
static boolean	MixedUtils.isColinear(DataSet ds, boolean verbose)	Check each pair of variables to see if correlation is 1.
static DataSet	MixedUtils.makeContinuousData(DataSet dsMix)
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)

Constructors in edu.pitt.csb.mgm with parameters of type DataSet

Modifier	Constructor	Description
	IndTestMultinomialLogisticRegressionWald(DataSet data, double alpha, boolean preferLinear)
	Mgm(DataSet ds, double[] lambda)

Uses of DataSet in edu.pitt.csb.stability

Methods in edu.pitt.csb.stability with parameters of type DataSet

Modifier and Type	Method	Description
abstract Graph	DataGraphSearch.search(DataSet data)
Graph	SearchWrappers.FgesWrapper.search(DataSet ds)
Graph	SearchWrappers.MGMWrapper.search(DataSet ds)
Graph	SearchWrappers.PcStableWrapper.search(DataSet ds)
static cern.colt.matrix.DoubleMatrix2D	StabilityUtils.StabilitySearch(DataSet data, DataGraphSearch gs, int N, int b)
static cern.colt.matrix.DoubleMatrix2D	StabilityUtils.StabilitySearchPar(DataSet data, DataGraphSearch gs, int N, int b)

Uses of DataSet in edu.pitt.dbmi.algo.bayesian.constraint.search

Constructors in edu.pitt.dbmi.algo.bayesian.constraint.search with parameters of type DataSet

Modifier	Constructor	Description
	PagSamplingRfci(DataSet dataSet)

Uses of DataSet in edu.pitt.dbmi.algo.resampling

Methods in edu.pitt.dbmi.algo.resampling with parameters of type DataSet

Modifier and Type	Method	Description
void	GeneralResamplingSearch.setData(DataSet data)

Constructors in edu.pitt.dbmi.algo.resampling with parameters of type DataSet

Modifier	Constructor	Description
	GeneralResamplingSearch(DataSet data, int numberResampling)
	GeneralResamplingTest(DataSet data, Algorithm algorithm, int numberResampling, double percentResamplingSize, boolean resamplingWithReplacement, int edgeEnsemble, boolean addOriginalDataset)

Constructor parameters in edu.pitt.dbmi.algo.resampling with type arguments of type DataSet

Modifier	Constructor	Description
	GeneralResamplingSearch(List<DataSet> dataSets, int numberResampling)
	GeneralResamplingTest(List<DataSet> dataSets, MultiDataSetAlgorithm multiDataSetAlgorithm, int numberResampling, double percentResamplingSize, boolean resamplingWithReplacement, int edgeEnsemble, boolean addOriginalDataset)