adjust_ezdm_accuracy: Adjust Accuracy Counts for Contamination

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Adjust Accuracy Counts for Contamination

Description

Adjusts accuracy counts (n_upper, n_trials) by removing estimated contaminant trials using binomial sampling. Contaminant trials are assumed to produce correct responses at a fixed guess rate (e.g., 0.5 for 2AFC tasks).

Usage

adjust_ezdm_accuracy(n_upper, n_trials, contaminant_prop, guess_rate = 0.5)

Arguments

n_upper	Numeric. Count of upper boundary (correct) responses.
n_trials	Numeric. Total number of trials.
contaminant_prop	Numeric. Estimated proportion of contaminant trials (e.g., from the contaminant_prop column of ezdm_summary_stats()).
guess_rate	Numeric. Assumed accuracy rate for contaminant trials (random guessing). Default is 0.5 (appropriate for 2AFC tasks).

Details

Uses binomial sampling to estimate the number of contaminant trials and contaminant correct responses, then subtracts these from the raw counts. Because of the stochastic sampling, results will vary across calls unless a seed is set by the user.

Value

A 1-row data.frame with columns n_upper_adj and n_trials_adj (integers). When contaminant_prop is NA or <= 0, returns the original counts unchanged.

See Also

ezdm_summary_stats() for computing the summary statistics and contamination proportions

Examples

# Adjust accuracy for estimated 10% contamination
set.seed(42)
adjust_ezdm_accuracy(n_upper = 80, n_trials = 100, contaminant_prop = 0.1)

# In a pipeline with ezdm_summary_stats
# library(dplyr)
# mydata |>
#   group_by(subject) |>
#   reframe(ezdm_summary_stats(rt, response)) |>
#   mutate(adjust_ezdm_accuracy(n_upper, n_trials, contaminant_prop))

bmm documentation built on March 30, 2026, 5:08 p.m.