lavaan (version 0.6-1.1161)

lavOptions: lavaan Options

Description

Show the default options used by the lavaan() function. The options can be changed by passing 'name = value' arguments to the lavaan() function call, where they will be added to the '...' argument.

Usage

lavOptions(x = NULL, default = NULL, mimic = "lavaan")

Arguments

x

Character. A character string holding an option name, or a character string vector holding multiple option names. All option names are converted to lower case.

default

If a single option is specified but not available, this value is returned.

mimic

Character. Adjust the option list for this mimic flag.

Details

This is the full list of options that are accepted by the lavaan() function, organized in several sections:

Model features (always available):

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.

int.ov.free:

If FALSE, the intercepts of the observed variables are fixed to zero.

int.lv.free:

If FALSE, the intercepts of the latent variables are fixed to zero.

conditional.x:

If TRUE, we set up the model conditional on the exogenous `x' covariates; the model-implied sample statistics only include the non-x variables. If FALSE, the exogenous `x' variables are modeled jointly with the other variables, and the model-implied statistics refect both sets of variables. If "default", the value is set depending on the estimator, and whether or not the model involves categorical endogenous variables.

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, variances and covariances are free parameters. If "default", the value is set depending on the mimic option.

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 (residual) 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.

parameterization:

Currently only used if data is categorical. If "delta", the delta parameterization is used. If "theta", the theta parameterization is used.

Model features (only available for the lavaan() function):

auto.fix.first:

If TRUE, the factor loading of the first indicator is set to 1.0 for every latent variable.

auto.fix.single:

If TRUE, the residual variance (if included) of an observed indicator is set to zero if it is the only indicator of a latent variable.

auto.var:

If TRUE, the residual variances and the variances of exogenous latent variables are included in the model and set free.

auto.cov.lv.x:

If TRUE, the covariances of exogenous latent variables are included in the model and set free.

auto.cov.y:

If TRUE, the covariances of dependent variables (both observed and latent) are included in the model and set free.

auto.th:

If TRUE, thresholds for limited dependent variables are included in the model and set free.

auto.delta:

If TRUE, response scaling parameters for limited dependent variables are included in the model and set free.

Data options:

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 Maximum Likelihood (FIML) estimation is used using all available data in the data frame. This is only valid if the data are missing completely at random (MCAR) or missing at random (MAR). If "default", the value is set depending on the estimator and the mimic option.

Data summary options:

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 to TRUE if maximum likelihood estimation is used and likelihood="normal", and FALSE otherwise.

ridge:

Numeric. Small constant used for ridging. Only used if the sample covariance matrix is non positive definite.

Multiple group options:

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 missing, 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", "residuals", "residual.covariances", "lv.variances" or "lv.covariances", specifying the pattern of equality constraints across multiple groups.

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).

group.w.free:

Logical. If TRUE, the group frequencies are considered to be free parameters in the model. In this case, a Poisson model is fitted to estimate the group frequencies. If FALSE (the default), the group frequencies are fixed to their observed values.

Estimation options:

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" for unweighted least squares and "DWLS" for diagonally weighted least squares. These are the main options that affect the estimation. For convenience, the "ML" option can be extended as "MLM", "MLMV", "MLMVS", "MLF", and "MLR". The estimation will still be plain "ML", but now with robust standard errors and a robust (scaled) test statistic. For "MLM", "MLMV", "MLMVS", classic robust standard errors are used (se="robust.sem"); for "MLF", standard errors are based on first-order derivatives (se="first.order"); for "MLR", `Huber-White' robust standard errors are used (se="robust.huber.white"). In addition, "MLM" will compute a Satorra-Bentler scaled (mean adjusted) test statistic (test="satorra.bentler") , "MLMVS" will compute a mean and variance adjusted test statistic (Satterthwaite style) (test="mean.var.adjusted"), "MLMV" will compute a mean and variance adjusted test statistic (scaled and shifted) (test="scaled.shifted"), and "MLR" will compute a test statistic which is asymptotically equivalent to the Yuan-Bentler T2-star test statistic. Analogously, the estimators "WLSM" and "WLSMV" imply the "DWLS" estimator (not the "WLS" estimator) with robust standard errors and a mean or mean and variance adjusted test statistic. Estimators "ULSM" and "ULSMV" imply the "ULS" estimator with robust standard errors and a mean or mean and variance adjusted test statistic.

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 "normal", the normal likelihood approach is used. Here, the covariance matrix has been divided by N, and both standard errors and test statistics are based on N. If "default", it depends on the mimic option: if mimic="lavaan" or mimic="Mplus", normal likelihood is used; otherwise, wishart likelihood is used.

link:

Currently only used if estimator is MML. If "logit", a logit link is used for binary and ordered observed variables. If "probit", a probit link is used. If "default", it is currently set to "probit" (but this may change).

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 and the mimic option.

se:

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 "robust.sem", conventional robust standard errors are computed. If "robust.huber.white", standard errors are computed based on the `mlr' (aka pseudo ML, Huber-White) approach. If "robust", either "robust.sem" or "robust.huber.white" is used depending on the estimator, the mimic option, and whether the data are complete or not. If "boot" or "bootstrap", bootstrap standard errors are computed using standard bootstrapping (unless Bollen-Stine bootstrapping is requested for the test statistic; in this case bootstrap standard errors are computed using model-based bootstrapping). If "none", no standard errors are computed.

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. If "mean.var.adjusted" or "Satterthwaite", a mean and variance adjusted test statistic is compute. If "scaled.shifted", an alternative mean and variance adjusted test statistic is computed (as in Mplus version 6 or higher). If "boot" or "bootstrap" or "Bollen.Stine", the Bollen-Stine bootstrap is used to compute the bootstrap probability value of the test statistic. If "default", the value depends on the values of other arguments.

bootstrap:

Number of bootstrap draws, if bootstrapping is used.

do.fit:

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

Optimization options:

control:

A list containing control parameters passed to the external optimizer. By default, lavaan uses "nlminb". See the manpage of nlminb for an overview of the control parameters. If another (external) optimizer is selected, see the manpage for that optimizer to see the possible control parameters.

optim.method:

Character. The optimizer that should be used. For unconstrained optimization (the model syntax does not include any "==", ">" or "<" operators), the available options are "nlminb" (the default), "BFGS" and "L-BFGS-B". For constrained optimization, the only available option is "nlminb.constr".

Categorical estimation options:

zero.add:

A numeric vector containing two values. These values affect the calculation of polychoric correlations when some frequencies in the bivariate table are zero. The first value only applies for 2x2 tables. The second value for larger tables. This value is added to the zero frequency in the bivariate table. If "default", the value is set depending on the "mimic" option. By default, lavaan uses zero.add = c(0.5. 0.0).

zero.keep.margins:

Logical. This argument only affects the computation of polychoric correlations for 2x2 tables with an empty cell, and where a value is added to the empty cell. If TRUE, the other values of the frequency table are adjusted so that all margins are unaffected. If "default", the value is set depending on the "mimic". The default is TRUE.

zero.cell.warn:

Logical. Only used if some observed endogenous variables are categorical. If TRUE, give a warning if one or more cells of a bivariate frequency table are empty.

Starting values options:

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 (set to 0.05), and the residual variances of observed variables (set to half the observed variance). If "Mplus", we use a similar scheme, but the factor loadings are estimated using the fabin3 estimator (tsls) per factor. If start is a fitted object of class '>lavaan, the estimated values of the corresponding parameters will be extracted. If it is a model list, for example the output of the paramaterEstimates() function, the values of the est or start or ustart column (whichever is found first) will be extracted.

Check options:

check:

Character vector. If check includes "start", the starting values are checked for possibly inconsistent values (for example values implying correlations larger than one); if check includes "post", a check is performed after (post) fitting, to check if the solution is admissable.

Verbosity options:

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.

Miscellaneous:

model.type:

Set the model type: possible values are "cfa", "sem" or "growth". This may affect how starting values are computed, and may be used to alter the terminology used in the summary output, or the layout of path diagrams that are based on a fitted lavaan object.

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 close to "Mplus".

representation:

If "LISREL" the classical LISREL matrix representation is used to represent the model (using the all-y variant). No other options are available (for now).

See Also

lavaan

Examples

Run this code
# NOT RUN {
lavOptions()
lavOptions("std.lv")
lavOptions(c("std.lv", "orthogonal"))
# }

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