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Retrieve Precomputed Sample Size Results

Source: R/fct_get_sspLNIRT.R
get_sspLNIRT.Rd

Looks up precomputed [optim_sample()] results from the internal dataset [sspLNIRT.data]. For each target parameter / threshold pair, the function finds the matching configuration and returns the result whose minimum \(N\) is the bottleneck (i.e., the largest required sample size across all requested parameters).

When multiple target parameters are supplied, each is matched independently against the precomputed grid. The returned result corresponds to whichever single-parameter configuration demanded the largest \(N\), since that \(N\) guarantees all other parameters also meet their thresholds (RMSE is monotonically decreasing in \(N\)).

Usage

get_sspLNIRT(thresh, out.par, K, mu.alpha, meanlog.sigma2, rho)

Arguments

thresh

Numeric vector. Target RMSE threshold(s). Same length as `out.par`.

out.par

Character vector. Item parameter(s): each one of `"alpha"`, `"beta"`, `"phi"`, `"lambda"`.

K

Integer. Test length.

mu.alpha

Numeric. Population mean of the discrimination parameter.

meanlog.sigma2

Numeric. Mean of the log-normal distribution for \(\sigma^2\) (on the log scale).

rho

Numeric in \([-1, 1]\). Correlation between \(\theta\) and \(\zeta\).

Value

A list with components:

`object`

An object of class `"sspLNIRT"` as returned by [optim_sample()].

`design`

A list with the full set of parameter values used for the precomputation.

Details

Searches [sspLNIRT.data] row by row using approximate matching (`tol = 1e-3`) for numeric parameters. If no exact match is found, the error message reports which parameters differed in the closest available configuration.

## Bottleneck selection

After matching each `out.par[j]` / `thresh[j]` pair independently, the bottleneck result is selected by: 1. If any `N.min == "res.ub > thresh"`, that result is returned. 2. Otherwise, the result with the largest numeric `N.min` is returned. 3. Otherwise (all `N.min == "res.lb < thresh"`), the first such result is returned.

See also

[optim_sample()]; [sspLNIRT.data]; [available_configs()].

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
)
summary(result$object)
} # }