Adjust Accuracy Counts for Contamination

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).

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.

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.

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)
#>   n_upper_adj n_trials_adj
#> 1          70           86

# 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))