semTools (version 0.5-1)

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,
  baseline.model = NULL)

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, or to lavTestLRT.mi when comparing '>lavaan.mi models.

indices

logical indicating whether to return fit indices from the fitMeasures function.

baseline.model

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

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 '

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"))
# }
# NOT RUN {
# }