This function calculates p-value for multiple lineups by using function
calc_p_value().
Usage
calc_p_value_multi(
dat,
lineup_id,
detect,
n_sel,
alpha,
n_plot = 20,
n_sim = 50000,
dist = "dirichlet",
cache_env = NULL,
method = "linear_approx",
replace_0 = TRUE
)Arguments
- dat
Data.frame/Tibble. A data.frame or a tibble.
- lineup_id
Column of ids of lineup.
- detect
Column of whether the lineup is detected by the subject.
- n_sel
Column of the number of selections.
- alpha
Column of the parameter values used by the Dirichlet distribution.
- n_plot
Integer. Number of plots.
- n_sim
Integer. Number of simulation draws.
- dist
Character. Name of the distribution used for the attractiveness simulation. One of "uniform" and "dirichlet".
- cache_env
Environment. A provided environment for caching.
- method
Character. Method of p-value calculation. "linear_approx" for linear approximation and "simulate" for simulation.
- replace_0
Boolean. Whether to treat evaluations with number of selections equal to 0 as selecting all the plots.
Details
It is encouraged to provide a cache environment to boost up the performance.
The cache environment will remember the result corresponding to the
combinations of n_eval and n_sim. replace_0 can be
turned on to set evaluations with 0 selections to be false detection
with 20 selections.
There are two attractiveness distribution available, one is uniform distribution, another is Dirichlet distribution. Uniform distribution ensures the marginal distribution of the probability of every plot being selected is uniform. When \(\alpha = 1\), Dirichlet distribution ensures the probability of every plot being selected is evenly distributed in a standard \(M - 1\) simplex.
Examples
dat <- data.frame(unique_lineup_id = c(1,1,2),
detect_or_not = c(TRUE, FALSE, TRUE),
num_selection = c(1,1,2))
calc_p_value_multi(dat, unique_lineup_id, detect_or_not, num_selection)
#> # A tibble: 2 × 2
#> unique_lineup_id p_value
#> <dbl> <dbl>
#> 1 1 0.0952
#> 2 2 0.525