Build an object summarizing fit indices across multiple models
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, baseline.model = NULL)
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fitted |
nested |
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argsLRT |
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indices |
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baseline.model |
optional fitted |
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)
HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' fit1 <- cfa(HS.model, data = HolzingerSwineford1939) ## non-nested model m2 <- ' f1 =~ x1 + x2 + x3 + x4 f2 =~ x5 + x6 + x7 + x8 + x9 ' fit2 <- cfa(m2, data = HolzingerSwineford1939) compareFit(fit1, fit2, nested = FALSE) ## nested model comparisons: out <- measurementInvariance(model = HS.model, data = HolzingerSwineford1939, group = "school", quiet = TRUE) compareFit(out) ## 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$school HS.amelia <- amelia(HSMiss, m = 20, noms = "school") imps <- HS.amelia$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 strictly-positive robust test statistics compareFit(scalar = mgfit0, metric = mgfit1, config = mgfit2, argsLRT = list(asymptotic = TRUE, method = "satorra.bentler.2010")) ## End(Not run)
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