extractobjective: Extracting empirical objective functions for item selection

In stuart: Subtests Using Algorithmic Rummaging Techniques

Extracting empirical objective functions for item selection

Description

This is a bare-bones initial version of this approach.

Usage

extractobjective(
  x,
  n = 50,
  side = c("top", "bottom", "center"),
  skew = FALSE,
  scale = 1,
  ...
)

Arguments

x	A vector of values for which to determine the objective function (e.g. RMSEA).
n	Number of values to use in function determining. Defaults to 50.
side	Which side good values are located at. 'top' means large values are good (e.g. Reliability), 'bottom' means small values are good (e.g. RMSEA), and 'middle' means average values are good (e.g. factor correlations).
skew	Whether to account for skew in the distribution using the [sn::psn()] function. Defaults to FALSE, meaning a normal distribution is used.
scale	A numeric scale to use in weighting the objective component. Defaults to 1.
...	Additional arguments.

Value

Returns an object of class stuartEmpiricalObjective.

Author(s)

Martin Schultze

See Also

empiricalobjective, fixedobjective, objectivematrices

stuart documentation built on June 7, 2023, 6:12 p.m.