Removes the estimated contaminant trials from the accuracy counts, on the
assumption that contaminants respond correctly at guess_rate. A port of
bmm::adjust_ezdm_accuracy().
Arguments
- n_upper
Count of upper-boundary (correct) responses. Vectorised: the three count and proportion arguments recycle to a common length, one row per element, so the function takes the columns of a summary table directly.
- n_trials
Total number of trials.
- contaminant_prop
Estimated contaminant proportion, typically the
contaminant_propcolumn ofrt_summary().NAor<= 0returns that row's counts unchanged.- guess_rate
Accuracy assumed for a contaminant response, known from the design.
0.5for a two-alternative task. One value for every row.
Value
A data.frame with integer n_upper_adj and n_trials_adj, one
row per input element. A row whose n_upper or n_trials is NA comes
back NA.
Details
Stochastic by design. How many trials were contaminants, and how many of
those happened to be correct, are both binomial draws, so repeated calls
differ. That is faithful to the uncertainty in a mixture estimate, which a
point estimate would understate, and it matches bmm. There is no
set.seed() anywhere in rtprep; reproducibility is the caller's.
Each row draws independently. For a single row the two draws are made in
the same order as bmm::adjust_ezdm_accuracy(), so the two functions give
the same answer from the same random seed.
See also
rt_summary() for the counts and the proportion, ez_ddm() for
what to do with them.
Examples
set.seed(42)
adjust_accuracy(n_upper = 80, n_trials = 100, contaminant_prop = 0.1)
#> n_upper_adj n_trials_adj
#> 1 70 86
# one row per cell of a summary table
cells <- data.frame(
n_upper = c(80, 45), n_trials = c(100, 50), contaminant_prop = c(0.1, 0.2)
)
adjust_accuracy(cells$n_upper, cells$n_trials, cells$contaminant_prop)
#> n_upper_adj n_trials_adj
#> 1 75 92
#> 2 37 37