latent_factors: Extract latent factor posteriors from fit

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

Extract latent factor posteriors from fit

Description

Extract latent factor posteriors from fit

Usage

latent_factors(fit, ...)

## Default S3 method:
latent_factors(fit, summary = TRUE, probs = c(0.025, 0.5, 0.975), ...)

Arguments

fit	A fit sampled with latent factors: its draw matrix (⁠$draws⁠ or ⁠$samples⁠) holds columns named factor[observation,factor] and ⁠$.internal$latent_info⁠ records the layout. The default method reads any fit, of any class, that stores them this way.
...	Passed to methods.
summary	Logical; if TRUE, return summary statistics. If FALSE, return full posterior draws.
probs	Quantiles for summary. Default is c(0.025, 0.5, 0.975).

Value

If summary = TRUE, a data frame with columns for observation, factor, and summary statistics. If summary = FALSE, a matrix of posterior draws.

Examples

# A fit sampled with latent factors comes from a model package (tulpaRatio
# reads the latent_factor() spec). The accessor reads two things off it:
# draw columns named factor[observation,factor] and the latent layout. Here
# both are simulated for three observations on two factors.
set.seed(1)
cols <- sprintf("factor[%d,%d]", rep(1:3, 2), rep(1:2, each = 3))
draws <- matrix(rnorm(200 * 6, mean = rep(c(-1, 0, 1), 2), sd = 0.3),
                200, 6, byrow = TRUE, dimnames = list(NULL, cols))
fit <- structure(
  list(draws = draws,
       .internal = list(latent_info = list(type = "latent", n_factors = 2L,
                                           n_obs = 3L))),
  class = "tulpa_fit")

latent_factors(fit)
dim(latent_factors(fit, summary = FALSE))

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