Uses of Enum Class
edu.cmu.tetrad.search.harness.AnnotationNormalization

Packages that use AnnotationNormalization

Package	Description
edu.cmu.tetrad.search.harness

Uses of AnnotationNormalization in edu.cmu.tetrad.search.harness

Subclasses with type arguments of type AnnotationNormalization in edu.cmu.tetrad.search.harness

Modifier and Type	Class	Description
enum	AnnotationNormalization	How a raw per-(SNP, trait) annotation matrix is turned into the adjustment scores that EdgePriorFacade and EdgePriorScoreFacade scale by tau.

Methods in edu.cmu.tetrad.search.harness that return AnnotationNormalization

Modifier and Type	Method	Description
static AnnotationNormalization	AnnotationNormalization.valueOf(String name)	Returns the enum constant of this class with the specified name.
static AnnotationNormalization[]	AnnotationNormalization.values()	Returns an array containing the constants of this enum class, in the order they are declared.

Methods in edu.cmu.tetrad.search.harness with parameters of type AnnotationNormalization

Modifier and Type	Method	Description
static EdgePriorFacade	EdgePriorFacade.fromAnnotation(String[] snpNames, String[] traitNames, double[][] annotation, double tau, double alpha, AnnotationNormalization normalization)	Builds a locus prior from a per-(SNP, trait) annotation under a selectable normalisation.
static EdgePriorScoreFacade	EdgePriorScoreFacade.fromAnnotation(String[] snpNames, String[] traitNames, double[][] annotation, double tau, AnnotationNormalization normalization)	Builds a locus score prior from a per-(SNP, trait) annotation under a selectable normalisation, on the raw log-odds scale: beta_jk = tau * adjusted(annotation)_jk.
static EdgePriorScoreFacade	EdgePriorScoreFacade.fromAnnotationTollScaled(String[] snpNames, String[] traitNames, double[][] annotation, double tau, double lambda, int n, AnnotationNormalization normalization)	Builds a locus score prior on the toll-fraction scale under a selectable normalisation: beta_jk = tau * adjusted(annotation)_jk * lambda * log(n) / 2.