Struct ModelBuilderData 

pub struct ModelBuilderData<'a, M, Y, F>
where
    M: Glm,
    Y: Response<M>,
    F: 'static + Float,
{ /* private fields */ }

Holds the data and all the specifications for the model and provides functions to adjust the settings.

Implementations

impl<'a, M, Y, F> ModelBuilderData<'a, M, Y, F>

where M: Glm, Y: Response<M> + Copy, F: Float,

A builder to generate a Model object

pub fn linear_offset(self, linear_offset: Array1<F>) -> Self

Represents an offset added to the linear predictor for each data point. This can be used to control for fixed effects or in multi-level models.

pub fn freq_weights(self, freqs: Array1<usize>) -> Self

Frequency weights (a.k.a. counts) for each observation. Traditionally these are positive integers representing the number of times each observation appears identically.

pub fn var_weights(self, weights: Array1<F>) -> Self

Variance weights (a.k.a. analytic weights) of each observation. These could represent the inverse square of the uncertainties of each measurement.

pub fn no_constant(self) -> Self

Do not add a constant term to the design matrix

pub fn colinear_tol(self, tol: F) -> Self

Set the tolerance for the co-linearity check. The check can be effectively disabled by setting the tolerance to a negative value.

pub fn build(self) -> RegressionResult<Model<M, F>>

where M: Glm, F: Float,