lavTestLRT(object, ..., method = "default", A.method = "exact", H1 = TRUE, type = "Chisq", model.names = NULL)
anova(object, ...)
lavaan
.lavaan
."satorra.bentler.2001"
, "satorra.bentler.2010"
and
"satorra.2000"
. See details."exact"
and "delta"
. This is only used when method = "satorra.2000"
.
It determines how the Jacobian of the constraint function (the matrix A)
will be computed."Chisq"
, the test statistic for each
model is the (scaled or unscaled) model fit test statistic. If "Cf"
,
the test statistic for each model is computed by the
lavTablesFitCf
function.anova
function for lavaan objects simply calls the
lavTestLRT
function, which has a few additional arguments. If type = "Chisq"
and the test statistics are scaled, a
special scaled difference test statistic is computed. If method is
"satorra.bentler.2001"
, a simple approximation is used
described in Satorra \& Bentler (2001). In some settings,
this can lead to a negative test statistic. To ensure a positive
test statistic, we can use the method proposed by
Satorra \& Bentler (2010). Alternatively, when method is
"satorra.2000"
, the original formulas of Satorra (2000) are
used. Note that for the Satorra (2000) method, the models must
be nested in the parameter sense, while for the other methods, they
only need to be nested in the covariance matrix sense.
Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507-514.
Satorra, A., & Bentler, P. M. (2010). Ensuring postiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243-248.
HS.model <- '
visual =~ x1 + b1*x2 + x3
textual =~ x4 + b2*x5 + x6
speed =~ x7 + b3*x8 + x9
'
fit1 <- cfa(HS.model, data = HolzingerSwineford1939)
fit0 <- cfa(HS.model, data = HolzingerSwineford1939,
orthogonal = TRUE)
lavTestLRT(fit1, fit0)
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