lavaan: Fit a Latent Variable Model
In nietsnel/psindex: Parameter Stability Index

Description Usage Arguments Value References See Also Examples

Fit a latent variable model.

lavaan(model = NULL, data = NULL, ordered = NULL,
       sampling.weights   = NULL,
       sample.cov = NULL, sample.mean = NULL, sample.nobs = NULL,
       group = NULL, cluster = NULL,
       constraints = "", WLS.V = NULL, NACOV = NULL,
       slotOptions = NULL, slotParTable = NULL, slotSampleStats = NULL,
       slotData = NULL, slotModel = NULL, slotCache = NULL,
       sloth1 = NULL,
       ...)

`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 original data.frame.)
`sampling.weights`	A variable name in the data frame containing sampling weight information. Currently only available for non-clustered data. Sampling weights must be nonnegative, and sum to the total number of observations. If this is not the case, the weights will be rescaled so that they sum to the total number of observations. Only available if estimator is ML in combination with robust standard errors and a robust test statistic. By default, the estimator will be `"MLR"`.
`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. Note that if maximum likelihood estimation is used and `likelihood="normal"`, the user provided covariance matrix is internally rescaled by multiplying it with a factor (N-1)/N, to ensure that the covariance matrix has been divided by N. This can be turned off by setting the `sample.cov.rescale` argument to `FALSE`.
`sample.mean`	A sample mean vector. For a multiple group analysis, a list with a mean vector 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.
`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.

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

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/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)