Uses of Interface
edu.cmu.tetrad.search.unmix.ResidualRegressor

Packages that use ResidualRegressor

Package	Description
edu.cmu.tetrad.search.unmix

Uses of ResidualRegressor in edu.cmu.tetrad.search.unmix

Classes in edu.cmu.tetrad.search.unmix that implement ResidualRegressor

Modifier and Type	Class	Description
class	LinearQRRegressor	Linear regression using EJML's QR-based solve (stable).

Methods in edu.cmu.tetrad.search.unmix with parameters of type ResidualRegressor

Modifier and Type	Method	Description
static UnmixDiagnostics.BicDelta	UnmixDiagnostics.computeBicDeltaK1K2(DataSet data, EmUnmix.Config baseCfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster)	Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
static double[][]	ResidualUtils.residualMatrix(DataSet data, Graph g, ResidualRegressor reg)	Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given graph.
static double[][]	ResidualUtils.residualMatrix(DataSet data, Map<Node,List<Node>> parentsMap, ResidualRegressor reg)	Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given parent map.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor)	Runs the unmixing process on the provided dataset using the specified configuration and regressor.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch)	Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing the provided dataset within the specified range.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
static UnmixDiagnostics.StabilityResult	UnmixDiagnostics.stabilityAcrossRestarts(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster, int repeats, long seedBase)	Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.