Description Usage Arguments Details See Also Examples

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.

1 | ```
lavOptions(x = NULL, default = NULL, mimic = "lavaan")
``` |

`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. |

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`

, all covariances among latent variables are set to zero.`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`

:The default setting is

`"listwise"`

: all cases with missing values are removed listwise from the data before the analysis starts. This is only valid if the data are missing completely at random (MCAR). Therefore, it may not be the optimal choice, but it can be useful for a first run. If the estimator belongs to the ML family, another option is`"ml"`

(alias:`"fiml"`

or`"direct"`

). This corresponds to the so-called full information maximum likelihood approach (fiml), where we compute the likelihood case by case, using all available data from that case. Note that if the model contains exogenous observed covariates, and`fixed.x = TRUE`

(the default), all cases with any missing values on these covariates will be deleted first. The option`"ml.x"`

(alias:`"fiml.x"`

or`"direct.x"`

) is similar to`"ml"`

, but does not delete any cases with missing values for the exogenous covariates, even if`fixed.x = TRUE`

. (Note: all lavaan versions < 0.6 used`"ml.x"`

instead of`"ml"`

). If you wish to use multiple imputation, you need to use an external package (eg. mice) to generate imputed datasets, which can then be analyzed using the`semList`

function. The semTools package contains several functions to do this automatically. Another option (with continuous data) is to use`"two.stage"`

or`"robust.two.stage"`

. In this approach, we first estimate the sample statistics (mean vector, variance-covariance matrix) using an EM algorithm. Then, we use these estimated sample statistics as input for a regular analysis (as if the data were complete). The standard errors and test statistics are adjusted correctly to reflect the two-step procedure. The`"robust.two.stage"`

option produces standard errors and a test statistic that are robust against non-normality. If (part of) the data is categorical, and the estimator is from the (W)LS family, the only option (besides listwise deletion) is`"pairwise"`

. In this three-step approach, missingness is only an issue in the first two steps. In the first step, we compute thresholds (for categorical variables) and means or intercepts (for continuous variables) using univariate information only. In this step, we simply ignore the missing values just like in mean(x, na.rm = TRUE). In the second step, we compute polychoric/polyserial/pearson correlations using (only) two variables at a time. Here we use pairwise deletion: we only keep those observations for which both values are observed (not-missing). And this may change from pair to pair. By default, in the categorical case we use`conditional.x = TRUE`

. Therefore, any cases with missing values on the exogenous covariates will be deleted listwise from the data first. Finally, if the estimator is`"PML"`

, the available options are`"pairwise"`

,`"available.cases"`

and`"doubly.robust"`

. See the PML tutorial on the lavaan website for more information about these approaches.

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 (`information = "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 (`test="yuan.bentler.mplus"`

). 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`"first.order"`

, the information matrix is based on the outer product of the casewise scores. See also the options`"h1.information"`

and`"observed.information"`

for further control. If`"default"`

, the value is set depending on the estimator, the missing argument, and the mimic option.`h1.information`

:If

`"structured"`

(the default), the unrestricted (h1) information part of the (expected, first.order or observed if h1 is used) information matrix is based on the structured, or model-implied statistics (model-implied covariance matrix, model-implied mean vector, etc.). If`"unstructured"`

, the unrestricted (h1) information part is based on sample-based statistics (observed covariance matrix, observed mean vector, etc.)`observed.information`

:If

`"hessian"`

, the observed information matrix is based on the hessian of the objective function. If`"h1"`

, an approximation is used that is based on the observed information matrix of the unrestricted (h1) model.`se`

:If

`"standard"`

, conventional standard errors are computed based on inverting the (expected, observed or first.order) information matrix. 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`"Yuan.Bentler.Mplus"`

, a test statistic is computed that is asymptotically equal to the Yuan-Bentler scaled test statistic. 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 parameter table, 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.start`

:Logical. If

`TRUE`

, the starting values are checked for possibly inconsistent values (for example values implying correlations larger than one). If needed, a warning is given.`check.gradient`

:Logical. If

`TRUE`

, and the model converged, a warning is given if the optimizer decided that a (local) solution has been found, while not all elements of the (unscaled) gradient (as seen by the optimizer) are (near) zero, as they should be (the tolerance used is 0.001).`check.post`

:Logical. If

`TRUE`

, and the model converged, a check is performed after (post) fitting, to verify if the solution is admissable. This implies that all variances are non-negative, and all the model-implied covariance matrices are positive (semi-)definite. For the latter test, we tolerate a tiny negative eigenvalue that is smaller than .Machine$double.eps^(3/4), treating it as being zero.`check.vcov`

:Logical. If

`TRUE`

, and the model converged, we check if the variance-covariance matrix of the free parameters is positive definite. We take into account possible equality and acitive inequality constraints. If needed, a warning is given.

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).`implied`

:Logical. If

`TRUE`

, compute the model-implied statistics, and store them in the implied slot.`h1`

:Logical. If

`TRUE`

, compute the unrestricted model and store the unrestricted summary statistics (and perhaps a loglikelihood) in the h1 slot.`baseline:`

Not used yet.

`baseline.conditional.x.free.slopes`

:Logical. If

`TRUE`

, and`conditional.x = TRUE`

, the (default) baseline model will allow the slopestructure to be unrestricted.

1 2 3 | ```
lavOptions()
lavOptions("std.lv")
lavOptions(c("std.lv", "orthogonal"))
``` |

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