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.
lavOptions(x = NULL, default = NULL, mimic = "lavaan")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.
If a single option is specified but not available, this value is returned.
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.
orthogonal.y:If TRUE, all covariances among
endogenous latent variables only are set to zero.
orthogonal.x:If TRUE, all covariances among
exogenous latent variables only 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. If there are multiple
groups, std.lv = TRUE and "loadings" is included in
the group.equal argument, then only the latent variances
of the first group will be fixed to 1.0, while the latent
variances of other groups are set free.
effect.coding:Can be logical or character string. If
logical and TRUE, this implies
effect.coding = c("loadings", "intercepts"). If logical and
FALSE, it is set equal to the empty string.
If "loadings" is included, equality
constraints are used so that the average of the factor loadings (per
latent variable) equals 1. Note that this should not be used
together with std.lv = TRUE. If "intercepts" is
included, equality constraints are used so that the sum of the
intercepts (belonging to the indicators of a single latent variable)
equals zero.
As a result, the latent mean will be freely estimated and usually
equal the average of the means of the involved indicators.
ceq.simple:Logical. If TRUE, and no other
general (equality or inequality) constraints are used in the model,
simple equality constraints
are represented in the parameter table as duplicated free parameters
(instead of extra rows with op = "==").
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.
auto.efa:If TRUE, the necessary constraints are
imposed to make the (unrotated) exploratory factor analysis blocks
identifiable: for each block, factor variances are set to 1, factor
covariances are constrained to be zero, and factor loadings are
constrained to follow an echelon pattern.
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.
sampling.weights.normalization:If "none", the
sampling weights (if provided) will not be transformed. If "total",
the sampling weights are normalized by dividing by the total sum of
the weights, and multiplying again by the total sample size.
If "group", the sampling weights are normalized per group:
by dividing by the sum of the weights (in each group), and multiplying
again by the group size. The default is "total".
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", "composite.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
(normal theory) generalized least squares,
"WLS" for weighted least squares
(sometimes called ADF estimation), "ULS" for unweighted least
squares, "DWLS" for diagonally weighted least squares,
and "DLS" for distributionally-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:Not used yet. This is just a placeholder until the MML estimator is back.
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. If
the argument is a vector with two elements, the first element
is used for the computation of the standard errors, while the
second element is used for the (robust) test statistic.
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.) If
the argument is a vector with two elements, the first element
is used for the computation of the standard errors, while the
second element is used for the (robust) test statistic.
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. If
the argument is a vector with two elements, the first element
is used for the computation of the standard errors, while the
second element is used for the (robust) test statistic.
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:Character vector. See the documentation of
the lavTest function for a full list. Multiple names
of test statistics can be provided. If "default", the value
depends on the values of other arguments. See also the
lavTest function to extract (alternative)
test statistics from a fitted lavaan object.
scaled.test:Character. Choose the test statistic
that will be scaled (if a scaled test statistic is requested).
The default is "standard", but it could also be (for example)
"Browne.residual.nt".
gamma.n.minus.oneLogical. If TRUE, we divide the
Gamma matrix by N-1 (instead of the default N).
gamma.unbiasedLogical. If TRUE, we compute an
unbiased version for the Gamma matrix. Only available for single-level
complete data and when conditional.x = FALSE and
fixed.x = FALSE (for now).
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 or models with only linear
equality constraints (i.e., the model syntax
does not include any "==", ">" or "<" operators),
the available options are "nlminb" (the default), "BFGS",
"L-BFGS-B". These are all quasi-newton methods. A basic
implementation of Gauss-Newton is also available
(optim.method = "GN"). The latter is the default when
estimator = "DLS".
For constrained
optimization, the only available option is "nlminb.constr",
which uses an augmented Lagrangian minimization algorithm.
optim.force.converged:Logical. If TRUE, pretend
the model has converged, no matter what.
optim.dx.tolNumeric. Tolerance used for checking if the elements of the (unscaled) gradient are all zero (in absolute value). The default value is 0.001.
optim.gn.tol.x:Numeric. Only used when
optim.method = "GN". Optimization stops when
the root mean square of the difference between the old and new
parameter values are smaller than this tolerance value. Default is
1e-05 for DLS estimation and 1e-07 otherwise.
optim.gn.iter.max:Integer. Only used when
optim.method = "GN". The maximum number of GN iterations.
The default is 200.
bounds:Only used if optim.method = "nlminb".
If logical: FALSE implies no bounds are imposed on the parameters.
If TRUE, this implies bounds = "wide". If character,
possible options are "none" (the default), "standard",
"wide", "pos.var", "pos.ov.var", and
"pos.lv.var".
If bounds = "pos.ov.var", the observed variances are forced to be
nonnegative. If bounds = "pos.lv.var", the latent variances are
forced to be nonnegative. If bounds = "pos.var", both observed
and latent variances are forced to be nonnegative. If
bounds = "standard", lower and upper bounds are computed for
observed and latent variances, and factor loadings. If
bounds = "wide", lower and upper bounds are computed for
observed and latent variances, and factor loadings; but the range of
the bounds is enlarged (allowing again for slightly negative variances).
optim.bounds:List. This can be used instead of the
bounds argument to allow more control. Possible elements of the
list are lower, upper, lower.factor and
upper.factor. All of these accept a vector. The lower and
upper elements indicate for which type of parameters bounds
should be computed. Possible choice are "ov.var", "lv.var",
"loadings" and "covariances". The lower.factor and
upper.factor elements should have the same length as the
lower and upper elements respectively. They indicate the
factor by which the range of the bounds should be enlarged (for
example, 1.1 or 1.2; the default is 1.0). Other elements are
min.reliability.marker which sets the lower bound for the
reliability of the marker indicator (if any) of each factor
(default is 0.1). Finally, the min.var.lv.endo element indicates
the lower bound of the variance of any endogenous latent variance
(default is 0.0).
Parallelization options (currently only used for bootstrapping):
The type of parallel operation to be used (if any). If
missing, the default is "no".
Integer: number of processes to be used in parallel operation:
typically one would chose this to the number of available CPUs. By
By default this is the number of cores (as detected by
parallel::detectCores()) minus one.
An optional parallel or snow cluster for use if
parallel = "snow". If not supplied, a cluster on the local
machine is created for the duration of the bootstrapLavaan
or bootstrapLRT call.
An integer to set the seed. Or NULL if no reproducible
results are needed. This works for both serial (non-parallel) and
parallel settings. Internally, RNGkind() is set to
"L'Ecuyer-CMRG" if parallel = "multicore". If
parallel = "snow" (under windows),
parallel::clusterSetRNGStream() is called which automatically
switches to "L'Ecuyer-CMRG". When iseed is not
NULL, .Random.seed (if it exists) in the global environment is
left untouched.
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 and
(residual) variances, which are set to one.
When start is "Mplus", the factor loadings are
estimated using the fabin3 estimator (tsls) per factor. The
residual variances of observed variables are set tot half the
observed variance, and all other (residual) variances are set to 0.05.
The remaining parameters (regression coefficients, covariances) are
set to zero.
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 admissible. 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.
check.lv.names:Logical. If TRUE, and latent variables
are defined in the model, lavaan will stop with an error message if
a latent variable name also occurs in the data (implying it is also
an observed variable).
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:Logical. If TRUE, compute a baseline model
(currently always the independence model, assuming all variables
are uncorrelated) and store the results in the baseline slot.
baseline.conditional.x.free.slopes:Logical. If TRUE,
and conditional.x = TRUE, the (default) baseline model will
allow the slopestructure to be unrestricted.
store.vcovLogical. If TRUE, and se= is not
set to "none", store the full variance-covariance matrix of
the model parameters in the vcov slot of the fitted lavaan object.
lavaan
lavOptions()
lavOptions("std.lv")
lavOptions(c("std.lv", "orthogonal"))
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