compareFit  R Documentation 
This function will create the template to compare fit indices across multiple fitted lavaan objects. The results can be exported to a clipboard or a file later.
compareFit(..., nested = TRUE, argsLRT = list(), indices = TRUE, moreIndices = FALSE, baseline.model = NULL, nPrior = 1)
... 
fitted 
nested 

argsLRT 

indices 

moreIndices 

baseline.model 
optional fitted 
nPrior 
passed to 
A FitDiff
object that saves model fit
comparisons across multiple models. If the models are not nested, only
fit indices for each model are returned. If the models are nested, the
differences in fit indices are additionally returned, as well as test
statistics comparing each sequential pair of models (ordered by their
degrees of freedom).
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Sunthud Pornprasertmanit (psunthud@gmail.com)
FitDiff
, clipboard
HS.model < ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' ## nonnested models fit1 < cfa(HS.model, data = HolzingerSwineford1939) m2 < ' f1 =~ x1 + x2 + x3 + x4 f2 =~ x5 + x6 + x7 + x8 + x9 ' fit2 < cfa(m2, data = HolzingerSwineford1939) (out1 < compareFit(fit1, fit2, nested = FALSE)) summary(out1) ## nested model comparisons: measurement equivalence/invariance fit.config < cfa(HS.model, data = HolzingerSwineford1939, group = "school") fit.metric < cfa(HS.model, data = HolzingerSwineford1939, group = "school", group.equal = "loadings") fit.scalar < cfa(HS.model, data = HolzingerSwineford1939, group = "school", group.equal = c("loadings","intercepts")) fit.strict < cfa(HS.model, data = HolzingerSwineford1939, group = "school", group.equal = c("loadings","intercepts","residuals")) measEqOut < compareFit(fit.config, fit.metric, fit.scalar, fit.strict, moreIndices = TRUE) # include moreFitIndices() summary(measEqOut) summary(measEqOut, fit.measures = "all") summary(measEqOut, fit.measures = c("aic", "bic", "sic", "ibic")) ## Not run: ## also applies to lavaan.mi objects (fit model to multiple imputations) set.seed(12345) HSMiss < HolzingerSwineford1939[ , paste("x", 1:9, sep = "")] HSMiss$x5 < ifelse(HSMiss$x1 <= quantile(HSMiss$x1, .3), NA, HSMiss$x5) HSMiss$x9 < ifelse(is.na(HSMiss$x5), NA, HSMiss$x9) HSMiss$school < HolzingerSwineford1939$school library(Amelia) HS.amelia < amelia(HSMiss, m = 20, noms = "school") imps < HS.amelia$imputations ## request robust test statistics mgfit2 < cfa.mi(HS.model, data = imps, group = "school", estimator = "mlm") mgfit1 < cfa.mi(HS.model, data = imps, group = "school", estimator = "mlm", group.equal = "loadings") mgfit0 < cfa.mi(HS.model, data = imps, group = "school", estimator = "mlm", group.equal = c("loadings","intercepts")) ## request the strictlypositive robust test statistics out2 < compareFit(scalar = mgfit0, metric = mgfit1, config = mgfit2, argsLRT = list(asymptotic = TRUE, method = "satorra.bentler.2010")) ## note that moreFitIndices() does not work for lavaan.mi objects, but the ## fitMeasures() method for lavaan.mi objects already returns gammaHat(s) summary(out2, fit.measures = c("ariv","fmi","df","crmr","srmr", "cfi.robust","tli.robust", "adjGammaHat.scaled","rmsea.ci.lower.robust", "rmsea.robust","rmsea.ci.upper.robust")) ## End(Not run)
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