wdm::Indep_test Class Reference

#include <wdm.hpp>

Public Member Functions
	Indep_test (std::vector< double > x, std::vector< double > y, std::string method, std::vector< double > weights=std::vector< double >(), bool remove_missing=true, std::string alternative="two-sided")
std::string	method () const
	the method used for the test
std::string	alternative () const
	the alternative hypothesis used for the test
double	n_eff () const
	the effective sample size in the test
double	estimate () const
	the estimated dependence measure
double	statistic () const
	the test statistic
double	p_value () const
	the p-value

Detailed Description

Independence test

The test calcualtes asymptotic p-values of independence tests based on (weighted) dependence measures.

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 \( D \)

Constructor & Destructor Documentation

wdm::Indep_test::Indep_test ( std::vector< double >  x, std::vector< double >  y, std::string  method, std::vector< double >  weights = std::vector<double>(), bool  remove_missing = true, std::string  alternative = "two-sided"  )

inline

Parameters

x,y	input data.
method	the dependence measure; see class 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.
alternative	indicates the alternative hypothesis and must be one of "two-sided","greater"or"less";"greater"corresponds to positive association,"less"to negative association. For Hoeffding's \form#3, only"two-sided"` is allowed.

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The documentation for this class was generated from the following file:

• include/wdm.hpp