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
The lavaan package version used to create this objects
The function call as returned by
The elapsed time (user+system) for various parts of the program as a list, including the total time.
Named list of options that were provided by the user, or filled-in automatically.
Named list describing the model parameters. Can be coerced to a data.frame. In the documentation, this is called the ‘parameter table’.
Named list containing parameter table attributes.
Object of internal class
about the data.
Object of internal class
Object of internal class
internal (matrix) representation of the model
List using objects that we try to compute only once, and reuse many times.
Object of internal class
results of fitting the model. No longer used.
List. Results and information about the bootstrap.
List. Information about the optimization.
List. Information about the loglikelihood of the model (if maximum likelihood was used).
List. Model implied statistics.
List. Information about the variance matrix (vcov) of the model parameters.
List. Different test statistics.
List. Information about the unrestricted h1 model (if available).
List. Information about a baseline model (often the independence model) (if available).
List. For internal use only.
List. Empty slot to be used by add-on packages.
signature(object = "lavaan", type = "free"): Returns
the estimates of the parameters in the model as a named numeric vector.
type="free", only the free parameters are returned.
type="user", all parameters listed in the parameter table
are returned, including constrained and fixed parameters.
signature(object = "lavaan"): Returns the
implied moments of the model as a list with two elements (per group):
cov for the implied covariance matrix,
mean for the implied mean
vector. If only the covariance matrix was analyzed, the implied mean
vector will be zero.
signature(object = "lavaan"): an alias for
signature(object = "lavaan", type="raw"):
type = "raw", this function returns the raw (= unscaled)
difference between the observed and the expected (model-implied) summary
type = "cor", or
type = "cor.bollen", the observed and
model implied covariance matrices are first transformed to a correlation
cov2cor()), before the residuals are computed.
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.
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
Unfortunately, the corresponding normalized residuals are not entirely
correct, and this option is only available for historical interest.
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.
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.
signature(object = "lavaan"): an alias
signature(object = "lavaan"): returns the
covariance matrix of the estimated parameters.
signature(object = "lavaan"): compute
factor scores for all cases that are provided in the data frame. For
complete data only.
signature(object = "lavaan"): returns
model comparison statistics. This method is just a wrapper around
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
signature(object = "lavaan", model, add, ...,
evaluate=TRUE): update a fitted lavaan object and evaluate it
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
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.
signature(object = "lavaan"):
returns the log-likelihood of the fitted model, if maximum likelihood estimation
was used. The
methods automatically work via
signature(object = "lavaan"): Print a short summary
of the model fit
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.
header = TRUE, the header section (including fit measures) is
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
for more details.
estimates = TRUE, print the parameter estimates section.
ci = TRUE, add confidence intervals to the parameter estimates
fmi = TRUE, add the fmi (fraction of missing information)
column, if it is available.
the standardized solution is also printed. Note that SEs and
tests are still based on unstandardized estimates. Use
standardizedSolution to obtain SEs and test
statistics for standardized estimates.
remove.step1, the parameters of the measurement part are not
shown (only used when using
rsquare=TRUE, the R-Square values for the dependent variables
in the model are printed.
std.nox = TRUE, the
std.all column contains the
std.nox column from the parameterEstimates() output.
efa = TRUE, EFA related information is printed. The related
efa.args list allows to set options related to the EFA output.
summary.efaList for more details.
modindices=TRUE, modification indices
are printed for all fixed parameters.
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. doi: 10.18637/jss.v048.i02
Standardized Residuals in Mplus. Document retrieved from URL https://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) summary(fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE) fitted(fit) coef(fit) resid(fit, type = "normalized")
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