wdm Namespace Reference

Weighted dependence measures. More...

Classes
class	Indep_test

Functions
double	wdm (const Eigen::VectorXd &x, const Eigen::VectorXd &y, std::string method, Eigen::VectorXd weights=Eigen::VectorXd(), bool remove_missing=true)
Eigen::MatrixXd	wdm (const Eigen::MatrixXd &x, std::string method, Eigen::VectorXd weights=Eigen::VectorXd(), bool remove_missing=true)
double	wdm (std::vector< double > x, std::vector< double > y, std::string method, std::vector< double > weights=std::vector< double >(), bool remove_missing=true)

Detailed Description

Weighted dependence measures.

Function Documentation

double wdm::wdm ( std::vector< double >  x, std::vector< double >  y, std::string  method, std::vector< double >  weights = std::vector<double>(), bool  remove_missing = true  )

inline

calculates (weighted) dependence measures.

Parameters

x,y	input data.
method	the dependence measure; see details for possible values.
weights	an optional vector of weights for the data.
remove_missing	if true, all observations containing a nan are removed; otherwise throws an error if nans are present.

Available methods:

• "pearson", "prho", "cor": Pearson correlation
• "spearman", "srho", "rho": Spearman's \( \rho \)
• "kendall", "ktau", "tau": Kendall's \( \tau \)
• "blomqvist", "bbeta", "beta": Blomqvist's \( \beta \)
• "hoeffding", "hoeffd", "d": Hoeffding's \( D \)

Returns

the dependence measure

double wdm::wdm ( const Eigen::VectorXd &  x, const Eigen::VectorXd &  y, std::string  method, Eigen::VectorXd  weights = Eigen::VectorXd(), bool  remove_missing = true  )

inline

calculates (weighted) dependence measures.

Parameters

x,y	input data.
method	the dependence measure; see details for possible values.
weights	an optional vector of weights for the data.
remove_missing	if true, all observations containing a nan are removed; otherwise throws an error if nans are present.

Available methods:

• "pearson", "prho", "cor": Pearson correlation
• "spearman", "srho", "rho": Spearman's \( \rho \)
• "kendall", "ktau", "tau": Kendall's \( \tau \)
• "blomqvist", "bbeta", "beta": Blomqvist's \( \beta \)
• "hoeffding", "hoeffd", "d": Hoeffding's \( D \)

Returns

the dependence measure

Eigen::MatrixXd wdm::wdm ( const Eigen::MatrixXd &  x, std::string  method, Eigen::VectorXd  weights = Eigen::VectorXd(), bool  remove_missing = true  )

inline

calculates a matrix of (weighted) dependence measures.

Parameters

x	input data.
method	the dependence measure; see details for possible values.
weights	an optional vector of weights for the data.
remove_missing	if true, all observations containing a nan are removed; otherwise throws an error if nans are present.

Available methods:

• "pearson", "prho", "cor": Pearson correlation
• "spearman", "srho", "rho": Spearman's \( \rho \)
• "kendall", "ktau", "tau": Kendall's \( \tau \)
• "blomqvist", "bbeta", "beta": Blomqvist's \( \beta \)
• "hoeffding", "hoeffd", "d": Hoeffding's \( D \)

Returns

a matrix of pairwise dependence measures.