Struct GLMResults 

pub struct GLMResults {

    pub coefficients: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub std_errors: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub z_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub p_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub fitted_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub residuals: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub pearson_residuals: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub hat_matrix_diag: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub scale: f64,
    pub deviance: f64,
    pub null_deviance: f64,
    pub df_residual: usize,
    pub df_null: usize,
    pub aic: f64,
    pub bic: f64,
    pub converged: bool,
    pub iterations: usize,
    pub family: Family,
    pub link: Link,
    pub intercept: bool,
    pub n_obs: usize,
    pub n_params: usize,
}

Results from fitting a Generalized Linear Model

Fields

coefficients: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Estimated coefficients

std_errors: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Standard errors of coefficients

z_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

z-statistics for coefficients (Wald test)

p_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

p-values for coefficients

fitted_values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Fitted values (on response scale)

residuals: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Residuals (response scale)

pearson_residuals: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Pearson residuals

hat_matrix_diag: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Diagonal of hat matrix (leverage)

scale: f64

Scale/dispersion parameter

deviance: f64

Residual deviance

null_deviance: f64

Null deviance

df_residual: usize

Residual degrees of freedom

df_null: usize

Null degrees of freedom

aic: f64

Akaike Information Criterion

bic: f64

Bayesian Information Criterion

converged: bool

Whether IRLS converged

iterations: usize

Number of IRLS iterations

family: Family

Distribution family

link: Link

Link function

intercept: bool

Whether intercept was included

n_obs: usize

Number of observations

n_params: usize

Number of parameters

Implementations

impl GLMResults

pub fn summary(&self, feature_names: &[String]) -> String

Create a summary string similar to R’s summary.glm()

pub fn coefficients_with_names( &self, feature_names: &[String], ) -> Vec<(String, f64)>

Get the coefficients as a vector with names

pub fn confidence_intervals( &self, alpha: f64, feature_names: &[String], ) -> Vec<(String, f64, f64)>

Get confidence intervals for coefficients

pub fn deviance_residuals( &self, y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, ) -> ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Compute deviance residuals

pub fn cooks_distance(&self) -> ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Compute Cook’s distances for influence diagnostics

pub fn standardized_pearson_residuals( &self, ) -> ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Compute standardized Pearson residuals

pub fn overdispersion_test(&self) -> (f64, f64)

Test for overdispersion (for Poisson and Binomial)

pub fn log_likelihood( &self, y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, ) -> f64

Compute log-likelihood (up to a constant)

pub fn pseudo_r_squared(&self) -> (f64, f64, f64)

Compute pseudo R-squared measures

pub fn likelihood_ratio_test(&self) -> (f64, f64)

Perform likelihood ratio test against null model

pub fn model_info(&self) -> String

Get model information as a string

Trait Implementations

impl Clone for GLMResults

fn clone(&self) -> GLMResults

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GLMResults

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for GLMResults

fn deserialize<__D>( __deserializer: __D, ) -> Result<GLMResults, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GLMResults

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.