prediction

Prediction helpers for brms models.

This module wraps brms prediction utilities and returns typed result objects that contain both an ArviZ InferenceData view and the underlying R result.

Notes

Executed inside the worker process that hosts the embedded R session.

Classes

Functions

posterior(model, **kwargs)

Return posterior draws as idata.

Wrapper around R posterior::as_draws_df().

Parameters:

Name	Type	Description	Default
model	FitResult	Fitted model.	required
**kwargs		Forwarded to posterior::as_draws_df(). e.g inc_warmup, regex, variable	{}

Returns:

Type	Description
PosteriorEpredResult	Result containing idata (ArviZ InferenceData) and an underlying R handle.

Examples:

from brmspy import brms

fit = brms.brm("y ~ x", data=df, chains=4)
ep = brms.posterior(fit)

ep.idata.posterior

posterior_epred(model, newdata=None, **kwargs)

posterior_epred(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None) -> IDResult[IDPosterior]

posterior_epred(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame) -> IDResult[IDPredictions]

Compute expected posterior predictions (noise-free).

Wrapper around R brms::posterior_epred(). This returns draws of the expected value (typically on the response scale), without observation noise.

Parameters:

Name	Type	Description	Default
model	FitResult	Fitted model.	required
newdata	DataFrame or None	New data for predictions. If None, uses the training data.	None
**kwargs		Forwarded to brms::posterior_epred().	{}

Returns:

Type	Description
PosteriorEpredResult	Result containing idata (ArviZ InferenceData) and an underlying R handle.

See Also

brms::posterior_epred : R documentation

Examples:

from brmspy import brms

fit = brms.brm("y ~ x", data=df, chains=4)
ep = brms.posterior_epred(fit)

ep.idata.posterior

posterior_predict(model, newdata=None, **kwargs)

posterior_predict(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDPosteriorPredictive]

posterior_predict(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDPredictions]

Draw from the posterior predictive distribution (includes observation noise).

Wrapper around R brms::posterior_predict().

Parameters:

Name	Type	Description	Default
model	FitResult	Fitted model.	required
newdata	DataFrame or None	New data for predictions. If None, uses the training data.	None
**kwargs		Forwarded to brms::posterior_predict().	{}

Returns:

Type	Description
PosteriorPredictResult	Result containing idata (ArviZ InferenceData) and an underlying R handle.

See Also

brms::posterior_predict : R documentation

Examples:

from brmspy import brms

fit = brms.brm("y ~ x", data=df, chains=4)
pp = brms.posterior_predict(fit)

pp.idata.posterior_predictive

posterior_linpred(model, newdata=None, **kwargs)

posterior_linpred(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDPosterior]

posterior_linpred(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDPredictions]

Draw from the linear predictor.

Wrapper around R brms::posterior_linpred(). This typically returns draws on the link scale (before applying the inverse link), unless you pass transform=True.

Parameters:

Name	Type	Description	Default
model	FitResult	Fitted model.	required
newdata	DataFrame or None	New data for predictions. If None, uses the training data.	None
**kwargs		Forwarded to brms::posterior_linpred() (commonly transform or ndraws).	{}

Returns:

Type	Description
PosteriorLinpredResult	Result containing idata (ArviZ InferenceData) and an underlying R handle.

See Also

brms::posterior_linpred : R documentation

Examples:

from brmspy import brms

fit = brms.brm("y ~ x", data=df, chains=4)
lp = brms.posterior_linpred(fit, transform=False)

lp.idata.predictions

log_lik(model, newdata=None, **kwargs)

log_lik(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDLogLikelihoodInsample]

log_lik(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDLogLikelihoodOutsample]

Compute pointwise log-likelihood draws.

Wrapper around R brms::log_lik(). The result is useful for LOO/WAIC via ArviZ.

Parameters:

Name	Type	Description	Default
model	FitResult	Fitted model.	required
newdata	DataFrame or None	New data. If None, uses the training data.	None
**kwargs		Forwarded to brms::log_lik().	{}

Returns:

Type	Description
LogLikResult	Result containing idata (ArviZ InferenceData) and an underlying R handle.

See Also

brms::log_lik : R documentation

Examples:

from brmspy import brms
import arviz as az

fit = brms.brm("y ~ x", data=df, chains=4)
ll = brms.log_lik(fit)

az.loo(ll.idata)