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FunctionalGlmResult

fdars_core::scalar_on_function

Struct FunctionalGlmResult 

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#[non_exhaustive]
pub struct FunctionalGlmResult {
Show 16 fields
    pub intercept: f64,
    pub beta_t: Vec<f64>,
    pub beta_se: Vec<f64>,
    pub gamma: Vec<f64>,
    pub fitted_values: Vec<f64>,
    pub linear_predictors: Vec<f64>,
    pub ncomp: usize,
    pub coefficients: Vec<f64>,
    pub std_errors: Vec<f64>,
    pub log_likelihood: f64,
    pub deviance: f64,
    pub iterations: usize,
    pub fpca: FpcaResult,
    pub aic: f64,
    pub bic: f64,
    pub family: GlmFamily,

}
Expand description

Result of functional_glm for a scalar response over functional predictors.

Contains the fitted model parameters, diagnostic statistics, and the embedded crate::regression::FpcaResult for projecting new data.

Fields (Non-exhaustive)§

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
§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)

§fitted_values: Vec<f64>

Fitted mean values μ = g⁻¹(η) (length n)

§linear_predictors: Vec<f64>

Linear predictors η = Xβ (length n)

§ncomp: usize

Number of FPC components used

§coefficients: Vec<f64>

All regression coefficients [intercept, γ₁…γ_K, z₁…z_P]

§std_errors: Vec<f64>

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

§log_likelihood: f64

Log-likelihood at convergence (kernel; see module doc for AIC comparability note)

§deviance: f64

GLM deviance D = 2(LL_saturated − LL_fitted)

§iterations: usize

Number of IRLS iterations performed

§fpca: FpcaResult

FPCA result (embedded for projecting new data)

§aic: f64

Akaike Information Criterion: −2·log_likelihood + 2·p

§bic: f64

Bayesian Information Criterion: −2·log_likelihood + p·ln(n)

§family: GlmFamily

Exponential-family distribution used for this fit

Implementations§

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impl FunctionalGlmResult

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

Predict response for new functional data. Delegates to predict_functional_glm.

§Errors

Propagates FdarError::InvalidDimension from predict_functional_glm when new_data / new_scalar shapes do not match the fitted model.

Trait Implementations§

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impl Clone for FunctionalGlmResult

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fn clone(&self) -> FunctionalGlmResult

Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§

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

Performs copy-assignment from source. Read more
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impl Debug for FunctionalGlmResult

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

Formats the value using the given formatter. Read more
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impl PartialEq for FunctionalGlmResult

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

Equality operator ==. Read more
1.0.0 (const: unstable) · Source§

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

Inequality operator !=. Read more
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impl StructuralPartialEq for FunctionalGlmResult

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