Extracts the optimization trace from an [optim_sample()] result, fits a log-log regression (\(\log(\mathrm{RMSE}) \sim \log(N)\)), and displays the relationship on both log-log and original scales side by side. The RMSE threshold and minimum \(N\) are overlaid as reference lines.
This function is preserved for backward compatibility. New code should prefer `plot(object, type = "power_curve", ...)`.
Arguments
- object
An object of class `"sspLNIRT"` containing a `$trace` element, as returned by [optim_sample()] or retrieved via [get_sspLNIRT()].
- out.par
Character or `NULL`. Which item parameter's RMSE trace to plot (one of `"alpha"`, `"beta"`, `"phi"`, `"lambda"`). When `NULL` (default), the first name of `object$res.best` is used; if that is also unavailable, an error is raised.
- thresh
Numeric. The RMSE threshold used in the optimization. Must be a single positive number.
See also
[plot.sspLNIRT()] for the recommended interface; [optim_sample()] for producing the trace; [plot_estimation()] for visualizing estimation accuracy by parameter value.
Examples
if (FALSE) { # \dontrun{
result <- get_sspLNIRT(
thresh = 0.10, out.par = "alpha",
K = 30, mu.alpha = 1,
meanlog.sigma2 = log(0.6), rho = 0.2
)
plot_power_curve(result$object, out.par = "alpha", thresh = 0.10)
# equivalent and preferred:
plot(result$object, type = "power_curve", out.par = "alpha", thresh = 0.10)
} # }