tabu.sem: Given a fitted lavaan model, a search table, and an objective...

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Given a fitted lavaan model, a search table, and an objective criterion, performs a Tabu model specification search. Currently only supports neighbors that are 1 move away from the current model.

Description

Given a fitted lavaan model, a search table, and an objective criterion, performs a Tabu model specification search. Currently only supports neighbors that are 1 move away from the current model.

Usage

tabu.sem(init.model, ptab, obj, niter = 30, tabu.size = 5)

Arguments

init.model	initial fitted model of class lavaan
ptab	search table (e.g., created by search.prep) that lists candidate parameters that can be modified as part of the search and how the parameters can be modified (fixed to what values)
obj	objective function to be MINIMIZED. Any function that takes a lavaan object as the sole argument and returns a numeric value can be used.
niter	number of Tabu iterations to perform
tabu.size	size of Tabu list

Value

A list with three elements: best.obj, the numerical value of the best (minimal) objective function achieved; best.mod, the final lavaan model, and best.binvec, a data.frame of the lavaan-formatted parameter table for the final model.

Author(s)

Carl F. Falk

References

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2017.1409074")}

Examples

# load simulation data and select columns used in this example
data(simulated_test_data)
tabuData <- simulated_test_data[, c(1:10)]

# specify an improper model (improper because data is unidimensional)
tabuModel <- "
Ability =~ Item1 + Item2 + Item3 + Item4
FakeAbility =~ Item5 + Item6 + Item7 + Item8
Ability ~ Outcome
FakeAbility ~ 0*Outcome"

# run the initial misspecified model for Tabu

init.model <- lavaan::lavaan(
  model = tabuModel, data = tabuData,
  auto.var = TRUE, auto.fix.first = FALSE, std.lv = TRUE, auto.cov.lv.x = TRUE
)

# Use search.prep to prepare for the Tabu search
ptab <- search.prep(fitted.model = init.model, loadings = TRUE, fcov = TRUE, errors = FALSE)

# Perform Tabu Search
trial <- tabu.sem(init.model = init.model, ptab = ptab, obj = AIC, niter = 2, tabu.size = 5)

AnthonyRaborn/ShortForm documentation built on Feb. 6, 2024, 1:25 a.m.