Struct PearsonCorrelation 

pub struct PearsonCorrelation {
    pub boolean: bool,
    pub significance_level: f64,
}

Pearson correlation conditional independence test.

Should be used only on continuous data. When the conditioning set is non-empty, uses linear regression to compute residuals and tests the Pearson correlation on those residuals (partial correlation).

References

• Pearson correlation coefficient
• Partial correlation using linear regression

Fields

boolean: bool

significance_level: f64

Implementations

impl PearsonCorrelation

pub fn new(boolean: bool, significance_level: f64) -> Self

Trait Implementations

impl CITest for PearsonCorrelation

fn run_test( &self, x_values: Array1<f64>, y_values: Array1<f64>, z: Array2<f64>, ) -> Result<TestResult>

Runs a conditional independence test on the given data.

fn data_types(&self) -> &'static [CITestDataType]

Data types that a test supports.

impl Clone for PearsonCorrelation

fn clone(&self) -> PearsonCorrelation

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PearsonCorrelation

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for PearsonCorrelation

fn eq(&self, other: &PearsonCorrelation) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for PearsonCorrelation