semTools (version 0.5-6)

compareFit: Build an object summarizing fit indices across multiple models

Description

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.

Usage

compareFit(..., nested = TRUE, argsLRT = list(), indices = TRUE,
  moreIndices = FALSE, baseline.model = NULL, nPrior = 1)

Arguments

...

fitted lavaan models or list(s) of lavaan objects. '>lavaan.mi objects are also accepted, but all models must belong to the same class.

nested

logical indicating whether the models in ... are nested. See net for an empirical test of nesting.

argsLRT

list of arguments to pass to lavTestLRT, as well as to lavTestLRT.mi and fitMeasures when comparing '>lavaan.mi models.

indices

logical indicating whether to return fit indices from the fitMeasures function. Selecting particular indices is controlled in the summary method; see '>FitDiff.

moreIndices

logical indicating whether to return fit indices from the moreFitIndices function. Selecting particular indices is controlled in the summary method; see '>FitDiff.

baseline.model

optional fitted '>lavaan model passed to fitMeasures to calculate incremental fit indices.

nPrior

passed to moreFitIndices, if relevant

Value

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).

See Also

'>FitDiff, clipboard

Examples

# NOT RUN {
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

## non-nested 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 strictly-positive 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"))
# }
# NOT RUN {
# }