Struct ndarray_glm::model::Model

pub struct Model<M, F> where
    M: Glm,
    F: Float,  {
    pub model: PhantomData<M>,
    pub y: Array1<F>,
    pub x: Array2<F>,
    pub linear_offset: Option<Array1<F>>,
    pub use_intercept: bool,
}

Holds the data and configuration settings for a regression.

Fields

model: PhantomData<M>

y: Array1<F>

the observation of response data by event

x: Array2<F>

the design matrix with events in rows and instances in columns

linear_offset: Option<Array1<F>>

The offset in the linear predictor for each data point. This can be used to fix the effect of control variables.

use_intercept: bool

Whether the intercept term is used (commonly true)

Implementations

impl<M, F> Model<M, F> where
    M: Glm,
    F: Float, 

pub fn fit(&self) -> RegressionResult<Fit<'_, M, F>>

Perform the regression and return a fit object holding the results.

pub fn fit_options(&self) -> FitConfig<'_, M, F>

Fit options builder interface

pub fn with_options(&self, options: FitOptions<F>) -> FitConfig<'_, M, F>

An experimental interface that would allow fit options to be set externally.

pub fn linear_predictor(&self, regressors: &Array1<F>) -> Array1<F>

Returns the linear predictors, i.e. the design matrix multiplied by the regression parameters. Each entry in the resulting array is the linear predictor for a given observation. If linear offsets for each observation are provided, these are added to the linear predictors