‘lavResiduals’ provides model residuals and standardized residuals from a fitted lavaan object, as well as various summaries of these residuals.
The ‘residuals()’ (and ‘resid()’) methods are just shortcuts to this function with a limited set of arguments.
lavResiduals(object, type = "cor.bentler", custom.rmr = NULL,
se = FALSE, zstat = TRUE, summary = TRUE, h1.acov = "unstructured",
add.type = TRUE, add.labels = TRUE, add.class = TRUE,
drop.list.single.group = TRUE,
maximum.number = length(res.vech), output = "list")If drop.list.single.group = TRUE, a list of (residualized) summary
statistics, including type, standardized residuals, and summaries. If
drop.list.single.group = FALSE, the list of summary statistics is nested
within a list for each group.
An object of class lavaan.
Character.
If type = "raw", this function returns the raw (= unscaled)
difference between the observed and the expected (model-implied) summary
statistics, as well as the standardized version of these residualds.
If type = "cor", or type = "cor.bollen", the observed and model
implied covariance matrices are first transformed to a correlation matrix
(using cov2cor()), before the residuals are computed.
If type = "cor.bentler", both the observed and model implied covariance
matrices are rescaled by dividing the elements by the square roots of the
corresponding variances of the observed covariance matrix.
list. Not used yet.
Logical. If TRUE, show the estimated standard errors
for the residuals.
Logical. If TRUE, show the standardized residuals, which
are the raw residuals divided by the corresponding (estimated) standard
errors.
Logical. If TRUE, show various summaries of the
(possibly scaled) residuals. When type = "raw", we compute the
RMR. When type = "cor.bentler",
we compute the SRMR. When type = "cor.bollen", we compute the CRMR.
An unbiased version of these summaries is also computed, as well as a
standard error, a z-statistic and a p-value for the test of exact fit
based on these summaries.
Character. If "unstructured", the observed summary
statistics are used as consistent estimates of the corresponding (unrestricted)
population statistics. If "structured", the model-implied summary
statistics are used as consistent estimates of the corresponding (unrestricted)
population statistics. This affects the way the asymptotic variance matrix
of the summary statistics is computed.
Logical. If TRUE, show the type of residuals
in the output.
If TRUE, variable names are added to the vectors
and/or matrices.
If TRUE, vectors are given the ‘lavaan.vector’
class; matrices are given the ‘lavaan.matrix’ class, and symmetric
matrices are given the ‘lavaan.matrix.symmetric’ class.
This only affects the way they are printed on the screen.
If FALSE, the results are returned as
a list, where each element corresponds to a group (even if there is only
a single group). If TRUE, the list will be unlisted if there is
only a single group.
Integer. Only used if output ="table". Show only
the first maximum.number rows of the data.frame.
Character. By default, output = "list", and the output
is a list of elements. If output = "table", only the residuals of
the variance-covariance matrix are shown in a data.frame, sorted from high
(in absolute value) to low.
Bentler, P.M. and Dijkstra, T. (1985). Efficient estimation via linearization in structural models. In Krishnaiah, P.R. (Ed.), Multivariate analysis - VI, (pp. 9--42). New York, NY: Elsevier.
Ogasawara, H. (2001). Standard errors of fit indices using residuals in structural equation modeling. Psychometrika, 66(3), 421--436. doi:10.1007/BF02294443
Maydeu-Olivares, A. (2017). Assessing the size of model misfit in structural equation models. Psychometrika, 82(3), 533--558. doi:10.1007/s11336-016-9552-7
Standardized Residuals in Mplus. Document retrieved from URL http://www.statmodel.com/download/StandardizedResiduals.pdf
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
lavResiduals(fit)
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