Enum LikelihoodScaleMetadata 

pub enum LikelihoodScaleMetadata {
    ProfiledGaussian,
    FixedDispersion {
        phi: f64,
    },
    FixedGammaShape {
        shape: f64,
    },
    EstimatedGammaShape {
        shape: f64,
    },
    EstimatedBetaPhi {
        phi: f64,
    },
    EstimatedTweediePhi {
        phi: f64,
    },
    EstimatedNegBinTheta {
        theta: f64,
    },
    FixedNegBinTheta {
        theta: f64,
    },
    Unspecified,
}

How a likelihood’s scale parameter is handled by the fit/result contract.

Variants

ProfiledGaussian

Gaussian identity fits profile sigma outside the fixed-scale GLM machinery.

FixedDispersion

Fixed exponential-dispersion parameter phi.

Fields

phi: f64

FixedGammaShape

Fixed Gamma shape k, equivalent to phi = 1 / k.

Fields

shape: f64

EstimatedGammaShape

Gamma shape k estimated jointly with the mean model.

Fields

shape: f64

EstimatedBetaPhi

Beta-regression precision phi estimated jointly with the mean model. Var(y) = mu(1-mu)/(1+phi); larger phi means less noise. Estimated from the working residuals after each mean fit and refreshed across outer iterations, exactly like the Gamma shape (issue #567).

Fields

phi: f64

EstimatedTweediePhi

Tweedie exponential-dispersion phi estimated jointly with the mean model. Var(y) = phi · mu^p with phi a genuine free parameter (unlike Binomial/Poisson, where phi ≡ 1). Estimated by the Pearson moment estimator phî = Σ wᵢ (yᵢ − μᵢ)² / μᵢ^p / Σ wᵢ at the converged η and refreshed across outer iterations, exactly like the Gamma shape and the Beta precision. phi enters the IRLS working weight prior·μ^{2−p}/phi, so the coefficient covariance Vb = H⁻¹ already scales as phi and the reported SEs track √phi (issue #771).

Fields

phi: f64

EstimatedNegBinTheta

Negative-Binomial overdispersion theta estimated jointly with the mean model. Var(y) = mu + mu^2 / theta; larger theta means less overdispersion (the Poisson limit is theta → ∞). Estimated by the maximum-likelihood theta score Σ wᵢ[ψ(yᵢ+θ) − ψ(θ) + lnθ + 1 − ln(θ+μᵢ) − (yᵢ+θ)/(μᵢ+θ)] = 0 at the converged η (MASS glm.nb’s theta.ml) and refreshed across outer iterations, exactly like the Gamma shape / Beta precision / Tweedie φ. Unlike those, theta is not a dispersion scale phi: it enters only the IRLS working weight W = μθ/(θ+μ) (the full NB2 Fisher information), so the stored penalized Hessian is already the true one and the coefficient covariance Vb = H⁻¹ takes no post-hoc multiply — phi ≡ 1 for NB, the overdispersion lives in the variance function. The theta carried here mirrors ResponseFamily::NegativeBinomial { theta } (the canonical store every weight/deviance expression reads), kept in sync by with_negbin_theta, exactly as EstimatedBetaPhi mirrors Beta { phi } (issue #802).

Fields

theta: f64

FixedNegBinTheta

Negative-Binomial overdispersion theta held fixed at a user-supplied value (--negative-binomial-theta, issue #983). Identical role to EstimatedNegBinTheta in every weight / variance / covariance expression (W = μθ/(θ+μ), Var(y) = μ + μ²/θ, phi ≡ 1), but the inner solver’s ML refresh is gated off: the recorded theta is the user’s, by construction. The fixed/estimated split mirrors FixedGammaShape vs EstimatedGammaShape.

Fields

theta: f64

Unspecified

The engine does not expose fixed-scale semantics for this family.

Implementations

impl LikelihoodScaleMetadata

pub const fn fixed_phi(self) -> Option<f64>

pub const fn negbin_theta_is_estimated(self) -> bool

Whether the Negative-Binomial overdispersion theta is estimated from data (the default for NB families, issue #802).

pub const fn negbin_theta(self) -> Option<f64>

The Negative-Binomial theta carried in the scale metadata (estimated or user-fixed), or None for non-NB families.

pub const fn beta_phi_is_estimated(self) -> bool

Whether the Beta-regression precision phi is estimated from data.

pub const fn tweedie_phi_is_estimated(self) -> bool

Whether the Tweedie exponential-dispersion phi is estimated from data.

pub const fn gamma_shape(self) -> Option<f64>

pub const fn gamma_shape_is_estimated(self) -> bool

Trait Implementations

impl Clone for LikelihoodScaleMetadata

fn clone(&self) -> LikelihoodScaleMetadata

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for LikelihoodScaleMetadata

impl Debug for LikelihoodScaleMetadata

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for LikelihoodScaleMetadata

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for LikelihoodScaleMetadata

fn eq(&self, other: &LikelihoodScaleMetadata) -> 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 Serialize for LikelihoodScaleMetadata

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.

impl StructuralPartialEq for LikelihoodScaleMetadata