Visualize performance criteria across sample sizes from an
irt_simulate() result. Calls summary.irt_results() internally,
then plots the requested criterion by sample size.
Usage
# S3 method for class 'irt_results'
plot(x, criterion = "rmse", param = NULL, item = NULL, threshold = NULL, ...)Arguments
- x
An
irt_resultsobject fromirt_simulate().- criterion
Character string. Which criterion to plot. Default
"rmse". Valid values:"bias","empirical_se","mse","rmse","coverage","mcse_bias","mcse_mse".- param
Optional character vector. Filter to specific parameter types (e.g.,
"a","b","b1").- item
Optional integer vector. Filter to specific item numbers.
- threshold
Optional numeric. If provided, draws a horizontal reference line at this value.
- ...
Additional arguments passed to
summary.irt_results().
Value
A ggplot2::ggplot object, returned invisibly.
See also
summary.irt_results() for the underlying criteria,
recommended_n() for sample-size recommendations.
Examples
# \donttest{
design <- irt_design(
model = "1PL", n_items = 5,
item_params = list(b = seq(-2, 2, length.out = 5))
)
study <- irt_study(design, sample_sizes = c(200, 500))
results <- irt_simulate(study, iterations = 10, seed = 42)
#> Iteration 5/10
#> Iteration 9/10
#> Iteration 10/10
#>
plot(results)
plot(results, criterion = "bias", threshold = 0.05, param = "b")
# }