lavResiduals {lavaan}

Residuals

Description

‘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.

Usage

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")

Arguments

object

An object of class lavaan.

type

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.

custom.rmr

list. Not used yet.

se

Logical. If TRUE, show the estimated standard errors for the residuals.

zstat

Logical. If TRUE, show the standardized residuals, which are the raw residuals divided by the corresponding (estimated) standard errors.

summary

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.

h1.acov

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.

add.type

Logical. If TRUE, show the type of residuals in the output.

add.labels

If TRUE, variable names are added to the vectors and/or matrices.

add.class

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.

drop.list.single.group

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.

maximum.number

Integer. Only used if output ="table". Show only the first maximum.number rows of the data.frame.

output

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.

Value

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.

References

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

Examples

HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data = HolzingerSwineford1939)
lavResiduals(fit)