Struct MultinomialFitOutputs 

pub struct MultinomialFitOutputs {
    pub coefficients_active: Array2<f64>,
    pub fitted_probabilities: Array2<f64>,
    pub iterations: usize,
    pub penalized_neg_log_likelihood: f64,
    pub deviance: f64,
    pub coefficient_covariance: Array2<f64>,
}

Outputs of fit_penalized_multinomial.

Fields

coefficients_active: Array2<f64>

Active-class coefficient block, shape (P, K-1) (column a is β_a). The reference class K - 1 has β_{K-1} ≡ 0 by construction and is not stored.

fitted_probabilities: Array2<f64>

Fitted probabilities, shape (N, K).

iterations: usize

Number of Newton iterations executed (including the final step that satisfied the tolerance). Non-convergence (outside the separation lane, which escalates to the Firth refit) is surfaced as the typed EstimationError::FixedLambdaNewtonDidNotConverge rather than an Ok with a flag, so every constructed value of this struct is a certified converged fit (SPEC: a fit only ever comes from a converged optimization).

penalized_neg_log_likelihood: f64

Penalized negative log-likelihood at the returned β̂: −log L(β̂) + ½ Σ_a λ_a · β̂_a^T S β̂_a.

deviance: f64

Unpenalized deviance −2 log L(β̂) for diagnostic reporting.

coefficient_covariance: Array2<f64>

Joint Laplace posterior coefficient covariance H⁻¹ at the converged β̂, shape (P·(K−1))×(P·(K−1)) (#1101). Block-ordered to match the stacked active-class coefficient vector β = [β_0; …; β_{K-2}]: active class a’s P coefficients occupy rows/cols a·P .. (a+1)·P, indexed θ[a·P + i] = β̂[i, a]. This is the Laplace covariance from the factored penalized Hessian XᵀWX + diag_a(λ_a)⊗S; it drives the delta-method per-class probability standard errors (Self::predict_probabilities_with_se) on the fixed-λ inner-solve path.

Implementations

impl MultinomialFitOutputs

pub fn n_active_classes(&self) -> usize

Number of active classes M = K − 1 (columns of Self::coefficients_active).

pub fn p_per_class(&self) -> usize

Per-class coefficient dimension P (rows of Self::coefficients_active).

pub fn predict_probabilities_with_se( &self, x_new: ArrayView2<'_, f64>, ) -> Result<(Array2<f64>, Array2<f64>), EstimationError>

Integrate the logistic-normal coefficient posterior at fresh design rows. Returns posterior-mean class probabilities and their exact-under-the- quadrature marginal standard deviations. The full joint coefficient covariance, including cross-class blocks, is contracted into each row’s active-logit covariance before deterministic adaptive integration.

pub fn predict_probabilities_with_se_and_control( &self, x_new: ArrayView2<'_, f64>, control: &MultinomialPosteriorIntegrationControl, ) -> Result<(Array2<f64>, Array2<f64>), EstimationError>

Trait Implementations

impl Clone for MultinomialFitOutputs

fn clone(&self) -> MultinomialFitOutputs

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MultinomialFitOutputs

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

Formats the value using the given formatter.