families

Reference: https://paulbuerkner.com/brms/reference/brmsfamily.html

Attributes¶

`FitResult = IDResult[IDBrm]` `module-attribute` ¶

Classes¶

`IDResult` `dataclass` ¶

Bases: Generic[T_idata], RListVectorExtension

Generic result container with arviz and R objects.

Attributes:

Name	Type	Description
`idata`	`InferenceData`	arviz InferenceData object
`r`	`ListVector`	R object from brms

Source code in brmspy/types/brms_results.py

@dataclass
class IDResult(Generic[T_idata], RListVectorExtension):
    """Generic result container with arviz and R objects.

    Attributes
    ----------
    idata : arviz.InferenceData
        arviz InferenceData object
    r : robjects.ListVector
        R object from brms
    """

    idata: T_idata

    def __repr__(self) -> str:
        return repr(self.idata)

Attributes¶

`idata` `instance-attribute` ¶

Functions¶

`repr()` ¶

Source code in brmspy/types/brms_results.py

def __repr__(self) -> str:
    return repr(self.idata)

`init(r, idata)` ¶

Functions¶

`kwargs_r(kwargs)` ¶

Convert Python keyword arguments to R-compatible format.

Convenience function that applies py_to_r() to all values in a keyword arguments dictionary, preparing them for R function calls.

Parameters:

Name	Type	Description	Default
`kwargs`	`dict or None`	Dictionary of keyword arguments where values may be Python objects (dicts, lists, DataFrames, arrays, etc.)	required

Returns:

Type	Description
`dict`	Dictionary with same keys but R-compatible values, or empty dict if None

Notes

This is a thin wrapper around py_to_r() that operates on dictionaries. It's commonly used to prepare keyword arguments for R function calls via rpy2.

Examples:

from brmspy.helpers.conversion import kwargs_r
import pandas as pd
import numpy as np

# Prepare kwargs for R function
py_kwargs = {
    'data': pd.DataFrame({'y': [1, 2], 'x': [1, 2]}),
    'prior': {'b': [0, 1]},
    'chains': 4,
    'iter': 2000
}

r_kwargs = kwargs_r(py_kwargs)
# All values converted to R objects
# Can now call: r_function(**r_kwargs)

`brmsfamily(family, link=None, link_sigma='log', link_shape='log', link_nu='logm1', link_phi='log', link_kappa='log', link_beta='log', link_zi='logit', link_hu='logit', link_zoi='logit', link_coi='logit', link_disc='log', link_bs='log', link_ndt='log', link_bias='logit', link_xi='log1p', link_alpha='identity', link_quantile='logit', threshold='flexible', refcat=None, **kwargs)` ¶

Family objects provide a convenient way to specify the details of the models used by many model fitting functions. The family functions presented here are for use with brms only and will not work with other model fitting functions such as glm or glmer. However, the standard family functions as described in family will work with brms.

Parameters:

Name	Type	Description	Default
`family`		A character string naming the distribution family of the response variable to be used in the model. Currently, the following families are supported: gaussian, student, binomial, bernoulli, beta-binomial, poisson, negbinomial, geometric, Gamma, skew_normal, lognormal, shifted_lognormal, exgaussian, wiener, inverse.gaussian, exponential, weibull, frechet, Beta, dirichlet, von_mises, asym_laplace, gen_extreme_value, categorical, multinomial, cumulative, cratio, sratio, acat, hurdle_poisson, hurdle_negbinomial, hurdle_gamma, hurdle_lognormal, hurdle_cumulative, zero_inflated_binomial, zero_inflated_beta_binomial, zero_inflated_beta, zero_inflated_negbinomial, zero_inflated_poisson, and zero_one_inflated_beta.	required
`link`	`str \| None`	A specification for the model link function. This can be a name/expression or character string. See the 'Details' section for more information on link functions supported by each family.	`None`
`link_sigma`	`str`	Link of auxiliary parameter sigma if being predicted.	`'log'`
`link_shape`	`str`	Link of auxiliary parameter shape if being predicted.	`'log'`
`link_nu`	`str`	Link of auxiliary parameter nu if being predicted.	`'logm1'`
`link_phi`	`str`	Link of auxiliary parameter phi if being predicted.	`'log'`
`link_kappa`	`str`	Link of auxiliary parameter kappa if being predicted.	`'log'`
`link_beta`	`str`	Link of auxiliary parameter beta if being predicted.	`'log'`
`link_zi`	`str`	Link of auxiliary parameter zi if being predicted.	`'logit'`
`link_hu`	`str`	Link of auxiliary parameter hu if being predicted.	`'logit'`
`link_zoi`	`str`	Link of auxiliary parameter zoi if being predicted.	`'logit'`
`link_coi`	`str`	Link of auxiliary parameter coi if being predicted.	`'logit'`
`link_disc`	`str`	Link of auxiliary parameter disc if being predicted.	`'log'`
`link_bs`	`str`	Link of auxiliary parameter bs if being predicted.	`'log'`
`link_ndt`	`str`	Link of auxiliary parameter ndt if being predicted.	`'log'`
`link_bias`	`str`	Link of auxiliary parameter bias if being predicted.	`'logit'`
`link_xi`	`str`	Link of auxiliary parameter xi if being predicted.	`'log1p'`
`link_alpha`	`str`	Link of auxiliary parameter alpha if being predicted.	`'identity'`
`link_quantile`	`str`	Link of auxiliary parameter quantile if being predicted.	`'logit'`
`threshold`	`str`	A character string indicating the type of thresholds (i.e. intercepts) used in an ordinal model. "flexible" provides the standard unstructured thresholds, "equidistant" restricts the distance between consecutive thresholds to the same value, and "sum_to_zero" ensures the thresholds sum to zero.	`'flexible'`
`refcat`	`str \| None`	Optional name of the reference response category used in categorical, multinomial, dirichlet and logistic_normal models. If NULL (the default), the first category is used as the reference. If NA, all categories will be predicted, which requires strong priors or carefully specified predictor terms in order to lead to an identified model.	`None`