lavResiduals: Residuals In lavaan: Latent Variable Analysis

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

 ```1 2 3 4 5``` ```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

Examples

 ```1 2 3 4 5 6``` ```HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' fit <- cfa(HS.model, data = HolzingerSwineford1939) lavResiduals(fit) ```

Example output

```This is lavaan 0.6-3
lavaan is BETA software! Please report any bugs.
\$type
[1] "cor.bentler"

\$cov
x1     x2     x3     x4     x5     x6     x7     x8     x9
x1  0.000
x2 -0.030  0.000
x3 -0.008  0.094  0.000
x4  0.071 -0.012 -0.068  0.000
x5 -0.009 -0.027 -0.151  0.005  0.000
x6  0.060  0.030 -0.026 -0.009  0.003  0.000
x7 -0.140 -0.189 -0.084  0.037 -0.036 -0.014  0.000
x8 -0.039 -0.052 -0.012 -0.067 -0.036 -0.022  0.075  0.000
x9  0.149  0.073  0.147  0.048  0.067  0.056 -0.038 -0.032  0.000

\$cov.z
x1     x2     x3     x4     x5     x6     x7     x8     x9
x1  0.000
x2 -1.996  0.000
x3 -0.997  2.689  0.000
x4  2.679 -0.284 -1.899  0.000
x5 -0.359 -0.591 -4.157  1.545  0.000
x6  2.155  0.681 -0.711 -2.588  0.942  0.000
x7 -3.773 -3.654 -1.858  0.865 -0.842 -0.326  0.000
x8 -1.380 -1.119 -0.300 -2.021 -1.099 -0.641  4.823  0.000
x9  4.077  1.606  3.518  1.225  1.701  1.423 -2.325 -4.132  0.000

\$summary
srmr srmr.se srmr.z srmr.pvalue usrmr usrmr.se
cov 0.065   0.006  6.063           0 0.058     0.01
```

lavaan documentation built on March 10, 2021, 5:05 p.m.