LRT test for comparing (nested) lavaan models.
lavTestLRT(object, ..., method = "default", A.method = "delta",
scaled.shifted = TRUE,
H1 = TRUE, type = "Chisq", model.names = NULL)
anova(object, ...)
Character string. The possible options are
"satorra.bentler.2001"
, "satorra.bentler.2010"
and
"satorra.2000"
. See details.
Not used yet
Character string. The possible options are "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. Note that if A.method = "exact"
, the models must
be nested in the parameter sense, while if A.method = "delta"
, they
only need to be nested in the covariance matrix sense.
Logical. Only used when method = "satorra.2000"
.
If TRUE
, we use a scaled and shifted test statistic; if FALSE
,
we use a mean and variance adjusted (Satterthwaite style) test statistic.
Character. If "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.
Character vector. If provided, use these model names in the first column of the anova table.
An object of class anova. When given a single argument, it simply returns the test statistic of this model. When given a sequence of objects, this function tests the models against one another in the order specified.
The 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.
Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In Heijmans, R.D.H., Pollock, D.S.G. & Satorra, A. (eds.), Innovations in multivariate statistical analysis. A Festschrift for Heinz Neudecker (pp.233-247). London: Kluwer Academic Publishers.
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.
# NOT RUN {
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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