Uses of Class
edu.cmu.tetrad.data.missing.MissingDataSpec

Packages that use MissingDataSpec

Package	Description
edu.cmu.tetrad.data.missing
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.score	This package contains classes for scoring causal graph models.
edu.cmu.tetrad.search.test	This package contains classes for testing causal graph search algorithms.

Uses of MissingDataSpec in edu.cmu.tetrad.data.missing

Methods in edu.cmu.tetrad.data.missing that return MissingDataSpec

Modifier and Type	Method	Description
static MissingDataSpec	MissingDataSpec.emCovariance()	A spec for EM covariance estimation with default EM settings.
static MissingDataSpec	MissingDataSpec.fail()	A spec that refuses data containing missing values.
static MissingDataSpec	MissingDataUtils.fromParameters(Parameters parameters)	Builds a MissingDataSpec from algcomparison/GUI/py-tetrad parameters (see the MISSING_* constants in Params), or returns null--meaning "legacy default behavior"--if the policy parameter is absent or "default".
static MissingDataSpec	MissingDataSpec.listwise()	A spec for listwise deletion.
static MissingDataSpec	MissingDataSpec.multipleImputation(int numImputations)	A spec for multiple imputation with the given number of imputations.
static MissingDataSpec	MissingDataSpec.of(MissingDataPolicy policy)	A spec with the given policy and default parameters.
static MissingDataSpec	MissingDataSpec.pairwiseCovariance()	A spec for a pairwise-deletion covariance matrix; see MissingDataPolicy.PAIRWISE_COVARIANCE and PairwiseCovarianceEstimator.
static MissingDataSpec	MissingDataSpec.serializableInstance()	Generates a simple exemplar of this class to test serialization.
static MissingDataSpec	MissingDataSpec.testwise()	A spec for test-wise deletion.
MissingDataSpec	MissingDataSpec.withEmMaxIterations(int emMaxIterations)	Returns a copy of this spec with the given maximum number of EM iterations.
MissingDataSpec	MissingDataSpec.withEmRidge(double emRidge)	Returns a copy of this spec with the given EM ridge.
MissingDataSpec	MissingDataSpec.withEmTolerance(double emTolerance)	Returns a copy of this spec with the given EM convergence tolerance.
MissingDataSpec	MissingDataSpec.withEssMode(MissingDataSpec.EffectiveSampleSizeMode essMode)	Returns a copy of this spec with the given effective sample size mode.
MissingDataSpec	MissingDataSpec.withNumImputations(int numImputations)	Returns a copy of this spec with the given number of imputations.
MissingDataSpec	MissingDataSpec.withSeed(long seed)	Returns a copy of this spec with the given random seed.

Methods in edu.cmu.tetrad.data.missing with parameters of type MissingDataSpec

Modifier and Type	Method	Description
static int	MissingDataUtils.effectiveSampleSize(DataSet dataSet, MissingDataSpec spec)	The effective sample size to use for penalized scores when the analysis runs on a covariance matrix estimated from incomplete data, per the spec's MissingDataSpec.EffectiveSampleSizeMode.
static ICovarianceMatrix	MissingDataUtils.estimateCovariance(DataSet dataSet, MissingDataSpec spec)	The covariance matrix of a dataset with missing values under a policy that estimates one: by EM with the spec's ridge, tolerance and iteration limit under EM_COVARIANCE, or by pairwise deletion (PairwiseCovarianceEstimator) under PAIRWISE_COVARIANCE.
static DataSet	MissingDataUtils.resolveDeletionPolicy(DataSet dataSet, MissingDataSpec spec, String caller)	Applies a missing-data specification to a dataset for a component that supports only the deletion policies natively: returns the dataset itself when it is complete or the policy is TESTWISE (the component then does its own per-family or per-test row selection), its complete cases under LISTWISE, and throws otherwise.
static MissingDataPolicy	MissingDataUtils.resolveOrWarn(DataSet dataSet, MissingDataSpec spec, String caller)	Resolves a possibly-null spec to a policy for a component that historically switched silently to test-wise deletion on missing data.
static ImputationSearch.Result	ImputationSearch.search(DataSet dataSet, Algorithm algorithm, Parameters parameters, MultipleImputer imputer, MissingDataSpec spec)	Runs the multiple-imputation search.

Constructors in edu.cmu.tetrad.data.missing with parameters of type MissingDataSpec

Modifier	Constructor	Description
	MvnImputer(MissingDataSpec spec)	Constructs an imputer whose EM settings (ridge, tolerance, max iterations) are taken from the given spec.

Uses of MissingDataSpec in edu.cmu.tetrad.search

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

Modifier and Type	Method	Description
MissingDataSpec	Rlcd.getMissingDataSpec()	The missing-data spec in force, or null.

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

Modifier and Type	Method	Description
void	Rlcd.setMissingDataSpec(MissingDataSpec spec)	Tells the search how to treat missing values in a single data set, by Tetrad's standard MissingDataSpec.

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

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

Modifier	Constructor	Description
	BasisFunctionBgeScore(DataSet dataSet, int truncationLimit, boolean adaptiveBasisSelection, MissingDataSpec spec)	As BasisFunctionBgeScore(DataSet, int, boolean), with an explicit missing-data specification.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda, boolean adaptiveBasisSelection, boolean rankTransform, MissingDataSpec spec)	As BasisFunctionBicScore(DataSet, int, double, boolean, boolean), with an explicit missing-data specification.
	BDeuScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	BgeScore(DataSet dataSet, MissingDataSpec spec)	Constructs a BGe score from a continuous data set with an explicit missing-data specification.
	ConditionalGaussianScore(DataSet dataSet, double penaltyDiscount, boolean discretize, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	DegenerateGaussianBgeScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score with an explicit missing-data specification; LISTWISE and TESTWISE are supported on data with missing values (see BasisFunctionBgeScore(DataSet, int, boolean, MissingDataSpec)).
	DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances, double lambda, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	DiscreteBicScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	MvpScore(DataSet dataSet, double structurePrior, int fDegree, boolean discretize, int effectiveSampleSize, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	SemBicScore(DataSet dataSet, boolean precomputeCovariances, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.

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

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

Modifier	Constructor	Description
	FfCi(DataSet dataSet, Parameters params, MissingDataSpec spec)	Constructs an instance with an explicit missing-data specification.
	FfCiContinuous(DataSet dataSet, MissingDataSpec spec)	Constructs FF-CI with an explicit missing-data specification.
	IndTestBasisFunctionBlocks(DataSet dataSet, int truncationLimit, int basisType, boolean adaptiveBasisSelection, MissingDataSpec spec)	As the four-argument constructor, with an explicit missing-data specification.
	IndTestBasisFunctionLrt(DataSet dataSet, int truncationLimit, double lambda, MissingDataSpec spec)	Deprecated. Constructs the test with an explicit missing-data specification.
	IndTestBlocksWilkes(BlockSpec blockSpec, MissingDataSpec spec)	Constructs the test with an explicit missing-data specification for the block data set.
	IndTestDegenerateGaussianLrt(DataSet dataSet, MissingDataSpec spec)	Constructs the test with an explicit missing-data specification.
	IndTestFisherZ(DataSet dataSet, double alpha, MissingDataSpec spec)	Constructs the test from a dataset with an explicit missing-data specification.
	Kci(DataSet dataSet, MissingDataSpec spec)	Constructs a Kci instance with an explicit missing-data specification.
	Rcit(DataSet dataSet, Parameters params, MissingDataSpec spec)	Constructs an instance with an explicit missing-data specification.