lavaan-class | R Documentation |

The `lavaan`

class represents a (fitted) latent variable
model. It contains a description of the model as specified by the user,
a summary of the data, an internal matrix representation, and if the model
was fitted, the fitting results.

Objects can be created via the
`cfa`

, `sem`

, `growth`

or
`lavaan`

functions.

`version`

:The lavaan package version used to create this objects

`call`

:The function call as returned by

`match.call()`

.`timing`

:The elapsed time (user+system) for various parts of the program as a list, including the total time.

`Options`

:Named list of options that were provided by the user, or filled-in automatically.

`ParTable`

:Named list describing the model parameters. Can be coerced to a data.frame. In the documentation, this is called the ‘parameter table’.

`pta`

:Named list containing parameter table attributes.

`Data`

:Object of internal class

`"Data"`

: information about the data.`SampleStats`

:Object of internal class

`"SampleStats"`

: sample statistics`Model`

:Object of internal class

`"Model"`

: the internal (matrix) representation of the model`Cache`

:List using objects that we try to compute only once, and reuse many times.

`Fit`

:Object of internal class

`"Fit"`

: the results of fitting the model. No longer used.`boot`

:List. Results and information about the bootstrap.

`optim`

:List. Information about the optimization.

`loglik`

:List. Information about the loglikelihood of the model (if maximum likelihood was used).

`implied`

:List. Model implied statistics.

`vcov`

:List. Information about the variance matrix (vcov) of the model parameters.

`test`

:List. Different test statistics.

`h1`

:List. Information about the unrestricted h1 model (if available).

`baseline`

:List. Information about a baseline model (often the independence model) (if available).

`internal`

:List. For internal use only.

`external`

:List. Empty slot to be used by add-on packages.

- coef
`signature(object = "lavaan", type = "free")`

: Returns the estimates of the parameters in the model as a named numeric vector. If`type="free"`

, only the free parameters are returned. If`type="user"`

, all parameters listed in the parameter table are returned, including constrained and fixed parameters.- fitted.values
`signature(object = "lavaan")`

: Returns the implied moments of the model as a list with two elements (per group):`cov`

for the implied covariance matrix, and`mean`

for the implied mean vector. If only the covariance matrix was analyzed, the implied mean vector will be zero.- fitted
`signature(object = "lavaan")`

: an alias for`fitted.values`

.- residuals
`signature(object = "lavaan", type="raw")`

: If`type = "raw"`

, this function returns the raw (= unscaled) difference between the observed and the expected (model-implied) summary statistics. 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. If`type="normalized"`

, the residuals are divided by the square root of the asymptotic variance of the corresponding summary statistic (the variance estimate depends on the choice for the`se`

argument). Unfortunately, the corresponding normalized residuals are not entirely correct, and this option is only available for historical interest. If`type="standardized"`

, the residuals are divided by the square root of the asymptotic variance of these residuals. The resulting standardized residuals elements can be interpreted as z-scores. If`type="standardized.mplus"`

, the residuals are divided by the square root of the asymptotic variance of these residuals. However, a simplified formula is used (see the Mplus reference below) which often results in negative estimates for the variances, resulting in many`NA`

values for the standardized residuals.- resid
`signature(object = "lavaan")`

: an alias for`residuals`

- vcov
`signature(object = "lavaan")`

: returns the covariance matrix of the estimated parameters.- predict
`signature(object = "lavaan")`

: compute factor scores for all cases that are provided in the data frame. For complete data only.- anova
`signature(object = "lavaan")`

: returns model comparison statistics. This method is just a wrapper around the function`lavTestLRT`

. If only a single argument (a fitted model) is provided, this model is compared to the unrestricted model. If two or more arguments (fitted models) are provided, the models are compared in a sequential order. Test statistics are based on the likelihood ratio test. For more details and further options, see the`lavTestLRT`

page.- update
`signature(object = "lavaan", model, add, ..., evaluate=TRUE)`

: update a fitted lavaan object and evaluate it (unless`evaluate=FALSE`

). Note that we use the environment that is stored within the lavaan object, which is not necessarily the parent frame. The`add`

argument is analogous to the one described in the`lavTestScore`

page, and can be used to add parameters to the specified model rather than passing an entirely new`model`

argument.- nobs
`signature(object = "lavaan")`

: returns the effective number of observations used when fitting the model. In a multiple group analysis, this is the sum of all observations per group.- logLik
`signature(object = "lavaan")`

: returns the log-likelihood of the fitted model, if maximum likelihood estimation was used. The`AIC`

and`BIC`

methods automatically work via`logLik()`

.- show
`signature(object = "lavaan")`

: Print a short summary of the model fit- summary
`signature(object = "lavaan", header = TRUE, fit.measures = FALSE, estimates = TRUE, ci = FALSE, fmi = FALSE, standardized = FALSE, remove.step1 = TRUE, cov.std = TRUE, rsquare = FALSE, std.nox = FALSE, modindices = FALSE, ci = FALSE, nd = 3L)`

: Print a nice summary of the model estimates. If`header = TRUE`

, the header section (including fit measures) is printed. If`fit.measures = TRUE`

, additional fit measures are added to the header section. The related`fm.args`

list allows to set options related to the fit measures. See`fitMeasures`

for more details. If`estimates = TRUE`

, print the parameter estimates section. If`ci = TRUE`

, add confidence intervals to the parameter estimates section. If`fmi = TRUE`

, add the fmi (fraction of missing information) column, if it is available. If`standardized=TRUE`

, the standardized solution is also printed. Note that*SE*s and tests are still based on unstandardized estimates. Use`standardizedSolution`

to obtain*SE*s and test statistics for standardized estimates. If`remove.step1`

, the parameters of the measurement part are not shown (only used when using`sam()`

.) If`rsquare=TRUE`

, the R-Square values for the dependent variables in the model are printed. If`std.nox = TRUE`

, the`std.all`

column contains the the`std.nox`

column from the parameterEstimates() output. If`efa = TRUE`

, EFA related information is printed. The related`efa.args`

list allows to set options related to the EFA output. See`summary.efaList`

for more details. If`modindices=TRUE`

, modification indices are printed for all fixed parameters. The argument`nd`

determines the number of digits after the decimal point to be printed (currently only in the parameter estimates section.) Historically, nothing was returned, but since 0.6-12, a list is returned of class`lavaan.summary`

for which is print function is available.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18637/jss.v048.i02")}

Standardized Residuals in Mplus. Document retrieved from URL https://www.statmodel.com/download/StandardizedResiduals.pdf

`cfa`

, `sem`

,
`fitMeasures`

, `standardizedSolution`

,
`parameterEstimates`

, `lavInspect`

,
`modindices`

```
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
summary(fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE)
fitted(fit)
coef(fit)
resid(fit, type = "normalized")
```

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