| search.prep | R Documentation |
Given a fitted lavaan model (e.g., CFA), prepares a table that contains parameters that can be fixed/freed as part of a model specification search.
search.prep(fitted.model, loadings = TRUE, fcov = TRUE, errors = FALSE)
fitted.model |
- an object of class "lavaan" that contains the initially fitted model for the search |
loadings |
- a logical value that indicates whether cross-loadings will be part of the search |
fcov |
- a logical value indicating whether factor covariances will be part of the search |
errors |
- a logical value indicating whether error covariances will be part of the search |
A data.frame with lavaan-formatted parameter values.
Carl F. Falk
https://doi.org/10.1080/10705511.2017.1409074
Other Tabu Search:
add.param(),
refit.model()
## Not run: # 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) # add an additional (mispecified) parameter additional.param <- 'Item1 ~~ 0.5*Item3' ptab <- add.param(fitted.model = init.model, ptab = ptab, syntax = additional.param) # Perform Tabu Search trial <- tabu.sem(init.model = init.model, ptab = ptab, obj = AIC, niter = 2, tabu.size = 5) ## End(Not run)