Fit a latent variable model.
lavaan(model = NULL, data = NULL, ordered = NULL, aux = NULL,
sampling_weights = NULL,
sample_cov = NULL, sample_mean = NULL, sample_th = NULL,
sample_nobs = NULL,
group = NULL, cluster = NULL, constraints = "",
wls_v = NULL, nacov = NULL, ov_order = "model",
slot_options = NULL, slot_par_table = NULL, slot_sample_stats = NULL,
slot_data = NULL, slot_model = NULL, slot_cache = NULL,
sloth1 = NULL,
...)An object of class lavaan, for which several methods
are available, including a summary method.
A description of the user-specified model. Typically, the model
is described using the lavaan model syntax. See
model.syntax for more information. Alternatively, a
parameter table (e.g., the output of the lavParTable() function) is also
accepted.
An optional data frame containing the observed variables used in the model. If some variables are declared as ordered factors, lavaan will treat them as ordinal variables.
Character vector. Only used if the data is in a data.frame. Treat these variables as ordered (ordinal) variables, if they are endogenous in the model. Importantly, all other variables will be treated as numeric (unless they are declared as ordered in the data.frame.) Since 0.6-4, ordered can also be logical. If TRUE, all observed endogenous variables are treated as ordered (ordinal). If FALSE, all observed endogenous variables are considered to be numeric (again, unless they are declared as ordered in the data.frame.)
Character vector. Names of auxiliary observed variables. Auxiliary
variables are not part of the user-specified model; they are used to make
the missing-at-random (MAR) assumption more plausible under missing data.
Only available (for now) with continuous data and one of
missing = "ml", "two.stage" or "robust.two.stage".
With missing = "ml" (full-information ML), the auxiliary variables
are added to the model as a block of `saturated correlates' (Graham, 2003):
each auxiliary variable is freely correlated with every other auxiliary
variable, with every model observed variable, and has a free mean. The
(enlarged) model is estimated with casewise FIML, so the auxiliary
variables affect both the point estimates and the standard errors, while
leaving the model degrees of freedom unchanged. The baseline model is
adjusted accordingly (so CFI/TLI remain correct), and the auxiliary
(nuisance) parameters are hidden from the default output (use
remove_aux = FALSE in parameterEstimates() to show them; they
are always part of coef()). With missing = "two.stage" or
"robust.two.stage", the auxiliary variables are instead included in
the first-stage EM run that estimates the saturated summary statistics the
model is fitted to, so they also affect the model estimates; the two-stage
standard errors account for the auxiliary variables via the augmented
first-stage asymptotic covariance (Savalei & Bentler, 2009), except when
fixed-x covariates are present (in which case they fall back to the
model-only covariance). Binary or ordered/categorical auxiliary variables
are not supported and are removed (with a warning).
A variable name in the data frame containing
sampling weight information. Currently only available for non-clustered
data. Depending on the sampling_weights.normalization option, these
weights may be rescaled (or not) so that their sum equals the number of
observations (total or per group).
Numeric matrix. A sample variance-covariance matrix.
The rownames and/or colnames must contain the observed variable names.
For a multiple group analysis, a list with a variance-covariance matrix
for each group. If conditional.x = TRUE, this is the
residual variance-covariance matrix (given the exogenous
covariates), and the regression intercepts, slopes and covariate moments
are provided through the attributes res.slopes, cov.x and
mean.x of sample_cov (with sample_mean holding the
residual intercepts). It is recommended to supply these attributes with
(dim)names, so that they can be matched against the model's internal
variable order; unnamed attributes are assumed to be in that internal
order already.
A sample mean vector. For a multiple group analysis, a list with a mean vector for each group.
Vector of sample-based thresholds. For a multiple group analysis, a list with a vector of thresholds for each group.
Number of observations if the full data frame is missing and only sample moments are given. For a multiple group analysis, a list or a vector with the number of observations for each group.
Character. A variable name in the data frame defining the groups in a multiple group analysis.
Character. A (single) variable name in the data frame defining the clusters in a two-level dataset.
Additional (in)equality constraints not yet included in the
model syntax. See model.syntax for more information.
A user provided weight matrix to be used by estimator "WLS";
if the estimator is "DWLS", only the diagonal of this matrix will be
used. For a multiple group analysis, a list with a weight matrix
for each group. The elements of the weight matrix should be in the
following order (if all data is continuous): first the means (if a
meanstructure is involved), then the lower triangular elements of the
covariance matrix including the diagonal, ordered column by column. In
the categorical case: first the thresholds (including the means for
continuous variables), then the slopes (if any), the variances of
continuous variables (if any), and finally the lower triangular elements
of the correlation/covariance matrix excluding the diagonal, ordered
column by column.
A user provided matrix containing the elements of (N times)
the asymptotic variance-covariance matrix of the sample statistics.
For a multiple group analysis, a list with an asymptotic
variance-covariance matrix for each group. See the wls_v
argument for information about the order of the elements.
Character. If "model" (the default), the order of
the observed variable names (as reflected for example in the output of
lav_object_vnames()) is determined by the model syntax. If
"data", the order is determined by the data (either the full
data.frame or the sample (co)variance matrix). If the wls_v
and/or nacov matrices are provided, this argument is currently
set to "data".
Options slot from a fitted lavaan object. If provided, no new Options slot will be created by this call.
ParTable slot from a fitted lavaan object. If provided, no new ParTable slot will be created by this call.
SampleStats slot from a fitted lavaan object. If provided, no new SampleStats slot will be created by this call.
Data slot from a fitted lavaan object. If provided, no new Data slot will be created by this call.
Model slot from a fitted lavaan object. If provided, no new Model slot will be created by this call.
Cache slot from a fitted lavaan object. If provided, no new Cache slot will be created by this call.
h1 slot from a fitted lavaan object. If provided, no new h1 slot will be created by this call.
Many more options can be specified, using 'name = value'.
See lavOptions for a complete list.
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. tools:::Rd_expr_doi("10.18637/jss.v048.i02")
cfa, sem, growth
# The Holzinger and Swineford (1939) example
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- lavaan(HS.model, data=HolzingerSwineford1939,
auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE)
summary(fit, fit.measures=TRUE)
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