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

Packages that use DataSet

Package	Description
edu.cmu.tetrad.algcomparison.simulation
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 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

Uses of DataSet in edu.cmu.tetrad.algcomparison.simulation

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

Modifier	Constructor	Description
	SingleDatasetSimulation(DataSet dataSet)	A Simulation implementation that returns a single supplied data set.

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)	Interpolates the given data set, producing a data set with no missing values.
DataSet	ModeInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	CellTableProbs.getDataSet()	Getter for the field dataSet.
DataSet	DataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	DirichletDataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	FactoredBayesStructuralEM.getDataSet()	Getter for the field dataSet.
DataSet	IntAveDataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	EmBayesEstimator.getMixedDataSet()	getMixedDataSet.
DataSet	BayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	DirichletBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set.
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved, int[] tiers)	Simulates a sample with the given sample size.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data for the given data set.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)	simulateData.
DataSet	MlBayesImObs.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	UpdatedBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.

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

Modifier and Type	Method	Description
static DirichletBayesIm	DirichletEstimator.estimate(DirichletBayesIm prior, DataSet dataSet)	estimate.
BayesIm	EmBayesProperties.Estimator.estimate(BayesPm bayesPm, DataSet dataSet)	estimate.
BayesIm	MlBayesEstimator.estimate(BayesPm bayesPm, DataSet dataSet)	Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
BayesIm	MlBayesEstimatorOld.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)	Interpolates the given data set, producing a data set with no missing values.
DataSet	ModeInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data for the given data set.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)	simulateData.
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.

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

Modifier	Constructor	Description
	BayesProperties(DataSet dataSet)	Constructs a new BayesProperties object for the given data set.
	BdeMetricCache(DataSet dataSet, BayesPm bayesPm)	Constructs a BdeMetricCache object for a given dataset and BayesPm.
	CellTableProbs(DataSet dataSet)	Creates a cell count table for the given data set.
	CptMapCounts(DataSet data)	Constructs a new CptMap based on counts from a given dataset.
	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)	Constructor for EmBayesEstimator.
	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)	Constructor for EmBayesProperties.
	FactoredBayesStructuralEM(DataSet dataSet, BayesPm bayesPmM0)	Constructor for FactoredBayesStructuralEM.
	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)	bootStrapSampling.

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

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

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)	The constructor sets the values of the private member variables.

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)	choleskySimulation.
static DataSet	DataTransforms.concatenate(DataSet... dataSets)	concatenate.
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)	concatenate.
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)	concatenate.
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)	convertNumericalDiscreteToContinuous.
DataSet	BoxDataSet.copy()	Returns a copy of this dataset.
DataSet	DataSet.copy()	Returns a copy of this dataset.
DataSet	NumberObjectDataSet.copy()	Returns a copy of this dataset.
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)	discretize.
DataSet	Discretizer.discretize()	discretize.
DataSet	CaseExpander.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)	getBootstrapSample.
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()	Getter for the field dataSet.
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)	getNonparanormalTransformed.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)	getResamplingDataset.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled without replacement.
DataSet	BoxDataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
DataSet	DataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
DataSet	NumberObjectDataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
static @NotNull DataSet	SimpleDataLoader.loadContinuousData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, 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, 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, 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)	removeConstantColumns.
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)	removeRandomColumns.
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)	replaceMissingWithRandom.
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)	restrictToMeasured.
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.scale(DataSet dataSet, double scale)	Scales the continuous variables in the given DataSet to have values in the range [-1, 1].
static DataSet	DataTransforms.scale(DataSet dataSet, double[] scales)
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)	shuffleColumns.
DataSet	Simulator.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
static DataSet	DataTransforms.standardizeData(DataSet dataSet)	standardizeData.
DataSet	BoxDataSet.subsetColumns(int[] indices)	Creates a new DataSet object containing only the specified columns.
DataSet	BoxDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	DataSet.subsetColumns(int[] columns)	subsetColumns.
DataSet	DataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	NumberObjectDataSet.subsetColumns(int[] indices)	subsetColumns.
DataSet	NumberObjectDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	BoxDataSet.subsetRows(int[] rows)	Creates a subset of rows from the existing DataSet.
DataSet	DataSet.subsetRows(int[] rows)	subsetRows.
DataSet	NumberObjectDataSet.subsetRows(int[] rows)	subsetRows.
DataSet	BoxDataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and columns.
DataSet	DataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and columns.
DataSet	NumberObjectDataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and 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)	center.
static List<DataSet>	DataSampling.createDataSamples(DataSet dataSet, Parameters parameters)	Create a list of dataset resampled from the given dataset.
static List<DataSet>	DataSampling.createDataSamples(DataSet dataSet, Parameters parameters, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Create a list of dataset resampled from the given dataset.
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)	shuffleColumns2.
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)	split.
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)	standardizeData.

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.
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
static DataSet	DataTransforms.concatenate(DataSet... dataSets)	concatenate.
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)	concatenate.
static boolean	DataUtils.containsMissingValue(DataSet data)	containsMissingValue.
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)	convertNumericalDiscreteToContinuous.
static void	DataTransforms.copyColumn(Node node, DataSet source, DataSet dest)	copyColumn.
static ICovarianceMatrix	DataTransforms.covarianceNonparanormalDrton(DataSet dataSet)	covarianceNonparanormalDrton.
static List<DataSet>	DataSampling.createDataSamples(DataSet dataSet, Parameters parameters)	Create a list of dataset resampled from the given dataset.
static List<DataSet>	DataSampling.createDataSamples(DataSet dataSet, Parameters parameters, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Create a list of dataset resampled from the given dataset.
static DataSet	DataTransforms.discretize(DataSet dataSet, int numCategories, boolean variablesCopied)	discretize.
DataSet	CaseExpander.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)	getBootstrapSample.
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)	getConstantColumns.
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCorrelationMatrix(DataSet dataSet)	getCorrelationMatrix.
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCovarianceMatrix(DataSet dataSet, boolean precomputeCovariances)	getCovarianceMatrix.
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)	getNonparanormalTransformed.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)	getResamplingDataset.
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)	removeConstantColumns.
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)	removeRandomColumns.
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)	replaceMissingWithRandom.
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)	restrictToMeasured.
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.scale(DataSet dataSet, double scale)	Scales the continuous variables in the given DataSet to have values in the range [-1, 1].
static DataSet	DataTransforms.scale(DataSet dataSet, double[] scales)
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)	shuffleColumns.
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)	split.
static DataSet	DataTransforms.standardizeData(DataSet dataSet)	standardizeData.
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)	center.
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)	concatenate.
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)	shuffleColumns2.
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)	standardizeData.

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

Modifier	Constructor	Description
	CellTableAdTree(DataSet dataSet, int[] testIndices)	Constructs a new CellTableAdTree using the provided data set and test indices.
	CellTableAdTree(DataSet dataSet, int[] testIndices, List<Integer> rows)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	CellTableCountSample(DataSet dataSet, int[] testIndices)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	CellTableCountSample(DataSet dataSet, int[] testIndices, List<Integer> rows)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	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)	Constructor for CovarianceMatrix.
	CovarianceMatrixOnTheFly(DataSet dataSet)	Constructs a new covariance matrix from the given data set.
	CovarianceMatrixOnTheFly(DataSet dataSet, boolean verbose)	Constructor for CovarianceMatrixOnTheFly.
	Discretizer(DataSet dataSet)	Constructs a new discretizer that discretizes every variable as binary, using evenly distributed values.
	Discretizer(DataSet dataSet, Map<Node,DiscretizationSpec> specs)	Constructor for Discretizer.
	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)	loadGraphBNTPcMatrix.

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)	residuals.

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

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

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()

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.
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA, boolean verbose)	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.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputCpdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given CPDAG (outputCpdag).
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
	BossLingam(Graph cpdag, DataSet dataSet)	Constructor.
	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)	Constructs a new instance of the FaskOrig class with the given DataSet and Score objects.
	FaskOrig(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 graph, List<Node> possibleCauses)	Constructor.
	IdaCheck(Graph graph, DataSet dataSet, SemIm trueSemIm)	Constructs a new IDA check for the given MPDAG and data set.

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 associated with this method.
DataSet	BdeuScore.getDataSet()	Retrieves the dataset associated with this BdeuScore object.
DataSet	DiscreteBicScore.getDataSet()	Returns the dataset.
DataSet	DiscreteBicScoreAdTree.getDataSet()	Returns the dataset.
DataSet	DiscreteScore.getDataSet()	Returns the dataset.
DataSet	GicScores.getDataSet()	Returns the dataset.
DataSet	IndTestScore.getDataSet()	Returns the data set.

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

Modifier and Type	Method	Description
static Matrix	SemBicScore.getCov(List<Integer> rows, int[] cols, int[] all, DataSet dataSet, Matrix cov)	Computes the covariance matrix for the given subset of rows and columns in the provided data set.

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

Modifier	Constructor	Description
	BasisFunctionBicScore(DataSet dataSet, boolean precomputeCovariances, int truncationLimit, int basisType, double basisScale)	Constructs a BasisFunctionBicScore object with the specified parameters.
	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)	Constructs the score using a dataset.
	DiscreteBicScore(DataSet dataSet)	Constructs the score using a dataset.
	DiscreteBicScoreAdTree(DataSet dataSet)	Constructs the score using a dataset.
	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)	Constructs the score using a data set.
	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()	Retrieves the data set.
DataSet	IndTestMvpLrt.getData()	Returns the data.
DataSet	IndTestRegression.getData()	Returns the data used.
DataSet	IndTestTrekSep.getData()	Gets the data set used for the independence test.
DataSet	MsepTest.getData()	Returns the data set used for the 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()	Returns the data sets used for the independence test.
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, ChiSquareTest.TestType testType, List<Integer> rows)	Constructs a test using the given data set and significance level.
	ConditionalCorrelationIndependence(DataSet dataSet, int basisType, double basisScale, int numFunctions)	Initializes a new instance of the ConditionalCorrelationIndependence class using the provided DataSet.
	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, double scalingFactor, int basisType, int numFunctions, double basisScale)	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)	ar.
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)	ar2.
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)	createShiftedData.
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
DataSet	TetradTest.getDataSet()	getDataSet.
DataSet	TetradTestContinuous.getDataSet()	Getter for the field dataSet.
DataSet	TetradTestDiscrete.getDataSet()	Getter for the field dataSet.
DataSet	TetradTestPopulation.getDataSet()	getDataSet.
DataSet	TsUtils.VarResult.getResiduals()	Getter for the field residuals.

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)	ar.
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)	ar2.
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)	createShiftedData.
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
static double[]	TsUtils.getSelfLoopCoefs(DataSet timeSeries)	getSelfLoopCoefs.
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)	Sets bandwidth from data using default method
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)	structuralVar.
static double	TsUtils.sumOfArCoefficients(DataSet timeSeries, int numLags)	sumOfArCoefficients.

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

Modifier	Constructor	Description
	AdTree(DataSet dataSet)	Constructs an AD Leaf Tree for the given dataset, without subsampling.
	AdTree(DataSet dataSet, List<Integer> rows)	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)	Constructor for Purify.
	TetradTestContinuous(DataSet dataSet, BpcTestType sigTestType, double sig)	Constructor for TetradTestContinuous.
	TetradTestDiscrete(DataSet dataSet, double sig)	Constructor for TetradTestDiscrete.
	VarResult(DataSet dataSet, Graph collapsedVarGraph)	Constructs a new result.

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()	Getter for the field data.
DataSet	IndTestCramerT.getData()	Retrieves the dataset used in the independence test.
DataSet	IndTestFisherZPercentIndependent.getData()	Retrieves the data set from the method.
DataSet	IndTestFisherZRecursive.getData()	getData.
DataSet	IndTestMixedMultipleTTest.getData()	Returne the original data for the method.
DataSet	IndTestMnlrLr.getData()	Returns the dataset.
DataSet	IndTestMultinomialLogisticRegression.getData()	Retrieves the original data used for the independence test.
DataSet	IndTestPositiveCorr.getData()	Retrieve the data set used in the independence test.
DataSet	IndTestSepsetDci.getData()	getData.
DataSet	ISBDeuScore.getDataSet()	Retrieves a DataSet object.
DataSet	ISBicScore.getDataSet()	Retrieves the data set from the current context.
DataSet	ISScore.getDataSet()	Retrieves the dataset used in the scoring calculations.
DataSet	SemBicScoreDeterministic.getDataSet()	getDataSet.
DataSet[]	MixtureModel.getDemixedData()	getDemixedData.

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()	Retrieves the list of data sets.
List<DataSet>	IndTestFisherZRecursive.getDataSets()	Returns the datasets for this test
List<DataSet>	IndTestPositiveCorr.getDataSets()	Retrieves the data sets used in the independence test.

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

Modifier and Type	Method	Description
void	DMSearch.setData(DataSet data)	Setter for the field 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.
	Demixer(DataSet data, int k)	Constructor.
	FasLofs(DataSet dataSet, Lofs.Rule rule)	Constructor for FasLofs.
	HbsmsBeam(Graph graph, DataSet data, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsGes(Graph graph, DataSet data)	Constructor for HbsmsGes.
	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).
	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)	Constructor for IndTestMixedMultipleTTest.
	IndTestMnlrLr(DataSet data, double alpha)	Constructs a new independence test for the given data set and significance level.
	IndTestMultinomialLogisticRegression(DataSet data, double alpha)	Constructor for IndTestMultinomialLogisticRegression.
	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).
	InverseCorrelation(DataSet dataSet, double threshold)	Constructor for InverseCorrelation.
	ISBDeuScore(DataSet dataSet, DataSet testCase)	Initializes the ISBDeuScore with the given dataset and test case.
	ISBicScore(DataSet dataSet, DataSet testCase)	Constructs an ISBicScore instance with the provided data sets.
	Kpc(DataSet dataset, double alpha)	Constructs a new PC search using for the given dataset.
	MagCgBicScore(DataSet dataSet)	Constructor.
	MagCgBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MagDgBicScore(DataSet dataSet)	Constructor.
	MagDgBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MagSemBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MixtureModel(DataSet data, double[][] dataArray, double[][] meansArray, double[] weightsArray, Matrix[] variancesArray, double[][] gammaArray)	Constructs a mixture model from a mixed data set, a means matrix, a weights array, a variance matrix, and a gamma matrix.
	Mmhc(IndependenceTest test, DataSet dataSet)	Constructor for Mmhc.
	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)	Initializes an object of the class IndTestFisherZPercentIndependent.

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()	Getter for the field dataSet.
DataSet	Scorer.getDataSet()	getDataSet.
DataSet	SemEstimator.getDataSet()	Getter for the field dataSet.
DataSet	GeneralizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data based on the given parameters.
DataSet	SemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
DataSet	StandardizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
DataSet	GeneralizedSemIm.simulateDataAvoidInfinity(int sampleSize, boolean latentDataSaved)	Simulates data avoiding infinity values.
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)	simulateDataFisher.
DataSet	GeneralizedSemIm.simulateDataMinimizeSurface(int sampleSize, boolean latentDataSaved)	Simulates data by minimizing the surface defined by the given sample size and whether latent data is saved.
DataSet	GeneralizedSemIm.simulateDataNSteps(int sampleSize, boolean latentDataSaved)	Simulates data for a given number of steps.
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)	simulateDataRecursive.
DataSet	LargeScaleSimulation.simulateDataReducedForm(int sampleSize)	Simulates data using the model X = (I - B)Y^-1 * e.
DataSet	SemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)	simulateDataReducedForm.
DataSet	StandardizedSemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)	simulateDataReducedForm.

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)	reidentifyVariables2.
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()	hybridsimulate.
DataSet	HsimContinuous.hybridsimulate()	hybridsimulate.

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

Modifier and Type	Method	Description
static VerticalIntDataBox	HsimUtils.makeVertIntBox(DataSet dataset)	makeVertIntBox.
void	GdistanceRandom.setLocationMap(DataSet map)	Setter for the field locationMap.

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

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

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)	getContinousData.
DataSet	IndTestMultinomialLogisticRegressionWald.getData()	Retrieves the original dataset used for the independence test.
static DataSet	MixedUtils.getDiscreteData(DataSet ds)	getDiscreteData.
static DataSet	MixedUtils.loadData(String dir, String filename)	loadData.
static DataSet	MixedUtils.loadDataSet(String dir, String filename)	loadDataSet.
static DataSet	MixedUtils.loadDelim(String dir, String filename)	loadDelim.
static DataSet	MixedUtils.makeContinuousData(DataSet dsMix)	makeContinuousData.
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)	makeMixedData.
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)	makeMixedData.

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)	getContinousData.
static int[]	MixedUtils.getDiscLevels(DataSet ds)	getDiscLevels.
static DataSet	MixedUtils.getDiscreteData(DataSet ds)	getDiscreteData.
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)	makeContinuousData.
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)	makeMixedData.
static DataSet	MixedUtils.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)	makeMixedData.

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

Modifier	Constructor	Description
	IndTestMultinomialLogisticRegressionWald(DataSet data, double alpha, boolean preferLinear)	Constructs a new instance of IndTestMultinomialLogisticRegressionWald with the specified parameters.
	Mgm(DataSet ds, double[] lambda)	Constructor for Mgm.

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)	search.
Graph	SearchWrappers.FgesWrapper.search(DataSet ds)	Search method.
Graph	SearchWrappers.MGMWrapper.search(DataSet ds)	Search method.
Graph	SearchWrappers.PcStableWrapper.search(DataSet ds)	Search method.
static cern.colt.matrix.DoubleMatrix2D	StabilityUtils.StabilitySearch(DataSet data, DataGraphSearch gs, int N, int b)	StabilitySearch.
static cern.colt.matrix.DoubleMatrix2D	StabilityUtils.StabilitySearchPar(DataSet data, DataGraphSearch gs, int N, int b)	StabilitySearchPar.

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)	Constructor.