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Compute Z-residuals for a Bernoulli/Logistic brms model

Source: R/Zresidual.bernoulli.brms.R
Zresidual_bernoulli_brms.Rd

Computes Z-residuals for a fitted Bayesian Bernoulli/Logistic (binary) model fitted with brms::brm() and family = bernoulli(). Z-residuals are calculated using posterior predictive methods and can be used for model diagnostics.

This is an internal workhorse for Zresidual.bernoulli.brms() and is not intended to be called directly by end users.

Usage

Zresidual_bernoulli_brms(
  fit,
  method = "iscv",
  n.rep = 1,
  data = NULL,
  type = NULL,
  ...
)

Arguments

fit

A fitted brmsfit model object with Bernoulli family.

method

Character string specifying the residual calculation method: "iscv" for importance-sampled cross-validated randomized predictive p-values, "rpost" for randomized posterior predictive p-values, or "mpost" for middle-value posterior predictive p-values. Default is "iscv".

n.rep

Integer; the number of replicated Z-residual sets to generate. Default is 1.

data

Optional data frame used to override the data stored inside fit for prediction and diagnostic calculation. If NULL, the data embedded in fit are used.

type

Optional character string controlling the residual type; the meaning is determined by the underlying implementation (if used).

...

Further arguments passed to lower-level helpers.

Value

A numeric matrix of Z-residuals with attributes:

  • type: Type of outcome (Bernoulli).

  • zero_id: Indices of zero outcomes.

  • log_pmf: Log-probability mass function values.

  • log_cdf: Log-cumulative distribution function values.

  • covariates: Model covariates.

  • linear.pred: Linear predictor values from the fitted model.

Details

The function typically performs the following steps:

  1. Extracts the observed response vector from the model data.

  2. Computes the log-PMF and log-CDF for the Bernoulli model using log_pred_dist_bern.

  3. Generates posterior predictive p-values according to the specified method.

  4. Converts the p-values to Z-residuals via the negative quantile of the standard normal distribution.

The output is a matrix of Z-residuals with one column per replication.

See also

log_pred_dist_bern, post_logrpp, iscv_logrpp, and the S3 wrapper Zresidual.bernoulli.brms().