lavaan: Fit a Latent Variable Model

In lavaan: Latent Variable Analysis

Fit a Latent Variable Model

Description

Fit a latent variable model.

Usage

lavaan(model = NULL, data = NULL, ordered = 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",
       slotOptions = NULL, slotParTable = NULL, slotSampleStats = NULL,
       slotData = NULL, slotModel = NULL, slotCache = NULL,
       sloth1 = NULL,
       ...)

Arguments

model	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 (eg. the output of the lavaanify() function) is also accepted.
data	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.
ordered	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.)
sampling.weights	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).
sample.cov	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.
sample.mean	A sample mean vector. For a multiple group analysis, a list with a mean vector for each group.
sample.th	Vector of sample-based thresholds. For a multiple group analysis, a list with a vector of thresholds for each group.
sample.nobs	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.
group	Character. A variable name in the data frame defining the groups in a multiple group analysis.
cluster	Character. A (single) variable name in the data frame defining the clusters in a two-level dataset.
constraints	Additional (in)equality constraints not yet included in the model syntax. See model.syntax for more information.
WLS.V	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.
NACOV	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.
ov.order	Character. If "model" (the default), the order of the observed variable names (as reflected for example in the output of lavNames()) 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".
slotOptions	Options slot from a fitted lavaan object. If provided, no new Options slot will be created by this call.
slotParTable	ParTable slot from a fitted lavaan object. If provided, no new ParTable slot will be created by this call.
slotSampleStats	SampleStats slot from a fitted lavaan object. If provided, no new SampleStats slot will be created by this call.
slotData	Data slot from a fitted lavaan object. If provided, no new Data slot will be created by this call.
slotModel	Model slot from a fitted lavaan object. If provided, no new Model slot will be created by this call.
slotCache	Cache slot from a fitted lavaan object. If provided, no new Cache slot will be created by this call.
sloth1	h1 slot from a fitted lavaan object. If provided, no new h1 slot will be created by this call.
...	Many more additional options can be defined, using 'name = value'. See lavOptions for a complete list.

Value

An object of class lavaan, for which several methods are available, including a summary method.

References

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")}

See Also

cfa, sem, growth

Examples

# 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)

lavaan documentation built on Sept. 27, 2024, 9:07 a.m.