Struct FunctionalLogisticResult 

pub struct FunctionalLogisticResult {

    pub intercept: f64,
    pub beta_t: Vec<f64>,
    pub beta_se: Vec<f64>,
    pub gamma: Vec<f64>,
    pub probabilities: Vec<f64>,
    pub predicted_classes: Vec<usize>,
    pub ncomp: usize,
    pub accuracy: f64,
    pub std_errors: Vec<f64>,
    pub coefficients: Vec<f64>,
    pub log_likelihood: f64,
    pub iterations: usize,
    pub fpca: FpcaResult,
    pub aic: f64,
    pub bic: f64,
}

Result of functional logistic regression.

Fields

intercept: f64

Intercept α

beta_t: Vec<f64>

Functional coefficient β(t), evaluated on the original grid (length m)

beta_se: Vec<f64>

Pointwise standard errors of β(t) (length m)

gamma: Vec<f64>

Scalar coefficients γ (one per scalar covariate)

probabilities: Vec<f64>

Predicted probabilities P(Y=1) (length n)

predicted_classes: Vec<usize>

Predicted class labels (0 or 1)

ncomp: usize

Number of FPC components used

accuracy: f64

Classification accuracy on training data

std_errors: Vec<f64>

Standard errors of all coefficients (intercept, FPC scores, scalar covariates)

coefficients: Vec<f64>

Regression coefficients on (FPC scores, scalar covariates) — internal

log_likelihood: f64

Log-likelihood at convergence

iterations: usize

Number of IRLS iterations

fpca: FpcaResult

FPCA result (for projecting new data)

aic: f64

Akaike Information Criterion

bic: f64

Bayesian Information Criterion

Implementations

impl FunctionalLogisticResult

pub fn predict( &self, new_data: &FdMatrix, new_scalar: Option<&FdMatrix>, ) -> Vec<f64>

Predict P(Y=1) for new data. Delegates to predict_functional_logistic.

Trait Implementations

impl Clone for FunctionalLogisticResult

fn clone(&self) -> FunctionalLogisticResult

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for FunctionalLogisticResult

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

Formats the value using the given formatter.

impl FpcPredictor for FunctionalLogisticResult

fn fpca_mean(&self) -> &[f64]

Mean function from FPCA (length m).

fn fpca_rotation(&self) -> &FdMatrix

Rotation matrix from FPCA (m × ncomp).

fn ncomp(&self) -> usize

Number of FPC components used.

fn training_scores(&self) -> &FdMatrix

Training FPC scores matrix (n × ncomp).

fn task_type(&self) -> TaskType

What kind of prediction task this model solves.

fn predict_from_scores( &self, scores: &[f64], scalar_covariates: Option<&[f64]>, ) -> f64

Predict from FPC scores + optional scalar covariates → single f64.

fn project(&self, data: &FdMatrix) -> FdMatrix

Project functional data to FPC scores.

impl PartialEq for FunctionalLogisticResult

fn eq(&self, other: &FunctionalLogisticResult) -> 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 FunctionalLogisticResult