tulpa_build_model_data: Build model matrices from a parsed formula

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Build model matrices from a parsed formula

Description

Takes a parsed formula and data frame, and constructs:

• The fixed-effects design matrix X

• An offset vector (or NULL) extracted from offset(...) terms

• RE group index vectors

• RE slope matrices (if applicable)

Usage

tulpa_build_model_data(parsed, data)

Arguments

parsed	A tulpa_parsed_formula object
data	A data frame

Details

Missing values pass through (na.action = na.pass), because a prior predictive may have no response; the fitting doors refuse them. Unused levels of a grouping factor are dropped, so n_groups counts the groups that have data.

Value

A list with:

• y: response vector (or NULL). A cbind(successes, failures) response is resolved to the successes column.

• n_trials: binomial denominators when the response was written as cbind(successes, failures), otherwise NULL

• X: fixed-effects design matrix

• offset: numeric vector or NULL

• re_terms: list of RE data structures (group indices, slope matrices)

Examples

d <- data.frame(y = rnorm(6), x = 1:6, g = rep(1:2, 3))
parsed <- tulpa_parse_formula(y ~ x + (1 | g))
md <- tulpa_build_model_data(parsed, d)
dim(md$X)
md$n_re_terms

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.