RMUreliability: Estimate reliability (Relative Measurement Uncertainty) from...

In easyRaschBayes: Bayesian Rasch Analysis Using 'brms'

Estimate reliability (Relative Measurement Uncertainty) from Bayesian measurement models

Description

This function measures reliability using posterior draws from a fitted Bayesian model.

Usage

RMUreliability(input_draws, verbose = FALSE, level = 0.95)

Arguments

input_draws	A matrix or data frame of posterior draws. Rows represent subjects and columns represent draws.
verbose	Logical. Print detailed information about the input data. Default is TRUE.
level	Numeric. Credibility level for the highest density continuous interval. Default is 0.95.

Details

To use this function, you will need to provide a matrix (input_draws) that contains the posterior draws for the parameter you wish to calculate reliability. The function assumes that rows of input_draws represent subjects and columns represent posterior draws.

For an example of how to apply this function to calculate mean score reliability using brms, see this tutorial.

For an example of how to apply this function to go/go-no task data using brms, see this tutorial.

Value

A list containing:

• hdci: A data frame with a point-estimate (posterior mean) and highest density continuous interval for reliability, calculated using the ggdist::mean_hdci function

• reliability_posterior_draws: A numeric vector of posterior draws for reliability, of length K/2 (K = number of columns/draws in your input_draws matrix)

References

Bignardi, G., Kievit, R., & Bürkner, P. C. (2025). A general method for estimating reliability using Bayesian Measurement Uncertainty. PsyArXiv. doi:10.31234/osf.io/h54k8

Examples

library(brms)
library(dplyr)
library(tidyr)
library(tibble)

# --- Dichotomous Rasch Model ---

df_rm <- eRm::raschdat3 %>%
  as.data.frame() %>%
  rownames_to_column("id") %>%
  pivot_longer(!id, names_to = "item", values_to = "response")

fit_rm <- brm(
  response ~ 1 + (1 | item) + (1 | id),
  data   = df_rm,
  family = bernoulli(),
  chains = 4,
  cores  = 1, # use more cores if you have
  iter   = 500 # use at least 2000 
)

# Extract posterior draws from brms model

posterior_draws = fit_rm %>%
  as_draws_df() %>%
  as_tibble() %>% 
  select(starts_with("r_id")) %>%
  t()

# Calculate RMU

RMUreliability(posterior_draws)

easyRaschBayes documentation built on March 28, 2026, 5:07 p.m.