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

Description Usage Arguments Value Author(s) References Examples

View source: R/Tabu_sem.R

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

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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

https://doi.org/10.1080/10705511.2017.1409074

Examples

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# 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)

ShortForm documentation built on March 26, 2020, 7:47 p.m.