LRT test
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, ...)
object |
An object of class |
... |
additional objects of class |
method |
Character string. The possible options are
|
H1 |
Not used yet |
A.method |
Character string. The possible options are |
scaled.shifted |
Logical. Only used when method = |
type |
Character. If |
model.names |
Character vector. If provided, use these model names in the first column of the anova table. |
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.
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.
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.
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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