sem: Fit Structural Equation Models

Description

Fit a Structural Equation Model (SEM).

Usage

sem(model = NULL, data = NULL,
    meanstructure = "default", fixed.x = "default",
    orthogonal = FALSE, std.lv = FALSE, std.ov = FALSE,
    missing = "default", ordered = NULL, 
    sample.cov = NULL, sample.cov.rescale = "default",
    sample.mean = NULL, sample.nobs = NULL, group = NULL, 
    group.label = NULL, group.equal = "", group.partial = "", 
    cluster = NULL, constraints = '', 
    estimator = "default", likelihood = "default", 
    information = "default", se = "default", test = "default",
    bootstrap = 1000L, mimic = "default", representation = "default", 
    do.fit = TRUE, control = list(), WLS.V = NULL, NACOV = NULL,
    start = "default", verbose = FALSE, warn = TRUE, debug = FALSE)

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 tab

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. This can be overriden by the ordered argument.

meanstructure

If TRUE, the means of the observed variables enter the model. If "default", the value is set based on the user-specified model, and/or the values of other arguments.

fixed.x

If TRUE, the exogenous `x' covariates are considered fixed variables and the means, variances and covariances of these variables are fixed to their sample values. If FALSE, they are considered random, and the means, v

orthogonal

If TRUE, the exogenous latent variables are assumed to be uncorrelated.

std.lv

If TRUE, the metric of each latent variable is determined by fixing their variances to 1.0. If FALSE, the metric of each latent variable is determined by fixing the factor loading of the first indicator to 1.0.

std.ov

If TRUE, all observed variables are standardized before entering the analysis.

missing

If "listwise", cases with missing values are removed listwise from the data frame before analysis. If "direct" or "ml" or "fiml" and the estimator is maximum likelihood, Full Information Maxi

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 (overriding any attributes in

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

sample.cov.rescale

If TRUE, the sample covariance matrix provided by the user is internally rescaled by multiplying it with a factor (N-1)/N. If "default", the value is set depending on the estimator and the likelihood option: it is set

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

A variable name in the data frame defining the groups in a multiple group analysis.

group.label

A character vector. The user can specify which group (or factor) levels need to be selected from the grouping variable, and in which order. If NULL (the default), all grouping levels are selected, in the order as they appear in the data.

group.equal

A vector of character strings. Only used in a multiple group analysis. Can be one or more of the following: "loadings", "intercepts", "means", "thresholds", "regressions",

group.partial

A vector of character strings containing the labels of the parameters which should be free in all groups (thereby overriding the group.equal argument for some specific parameters).

cluster

Not used yet.

constraints

Additional (in)equality constraints not yet included in the model syntax. See model.syntax for more information.

estimator

The estimator to be used. Can be one of the following: "ML" for maximum likelihood, "GLS" for generalized least squares, "WLS" for weighted least squares (sometimes called ADF estimation), "ULS"

likelihood

Only relevant for ML estimation. If "wishart", the wishart likelihood approach is used. In this approach, the covariance matrix has been divided by N-1, and both standard errors and test statistics are based on N-1. If

information

If "expected", the expected information matrix is used (to compute the standard errors). If "observed", the observed information matrix is used. If "default", the value is set depending on the estimator

If "standard", conventional standard errors are computed based on inverting the (expected or observed) information matrix. If "first.order", standard errors are computed based on first-order derivatives. If "rob

test

If "standard", a conventional chi-square test is computed. If "Satorra.Bentler", a Satorra-Bentler scaled test statistic is computed. If "Yuan.Bentler", a Yuan-Bentler scaled test statistic is computed. I

bootstrap

Number of bootstrap draws, if bootstrapping is used.

mimic

If "Mplus", an attempt is made to mimic the Mplus program. If "EQS", an attempt is made to mimic the EQS program. If "default", the value is (currently) set to to "lavaan", which is very clo

representation

If "LISREL" the classical LISREL matrix representation is used to represent the model (using the all-y variant).

do.fit

If FALSE, the model is not fit, and the current starting values of the model parameters are preserved.

control

A list containing control parameters passed to the optimizer. By default, lavaan uses "nlminb". See the manpage of nlminb for an overview of the control parameters. A different op

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 el

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 W

start

If it is a character string, the two options are currently "simple" and "Mplus". In the first case, all parameter values are set to zero, except the factor loadings (set to one), the variances of latent variables

verbose

If TRUE, the function value is printed out during each iteration.

warn

If TRUE, some (possibly harmless) warnings are printed out during the iterations.

debug

If TRUE, debugging information is printed out.

Value

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

Details

The sem function is a wrapper for the more general lavaan function, using the following default arguments: int.ov.free = TRUE, int.lv.free = FALSE, auto.fix.first = TRUE (unless std.lv = TRUE), auto.fix.single = TRUE, auto.var = TRUE, auto.cov.lv.x = TRUE, and auto.cov.y = TRUE.

References

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/.

Examples

Run this code

## The industrialization and Political Democracy Example 
## Bollen (1989), page 332
model <- '# latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + a*y2 + b*y3 + c*y4
     dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

  # residual correlations
    y1 ~~ y5
    y2 ~~ y4 + y6
    y3 ~~ y7
    y4 ~~ y8
    y6 ~~ y8
'

fit <- sem(model, data=PoliticalDemocracy)
summary(fit, fit.measures=TRUE)

Run the code above in your browser using DataLab