If all data is continuous, the default estimator in the lavaan package is
maximum likelihood (
estimator = "ML"). Alternative estimators available in
"GLS": generalized least squares. For complete data only.
"WLS": weighted least squares (sometimes called ADF estimation). For complete data only.
"DWLS": diagonally weighted least squares
"ULS": unweighted least squares
Many estimators have ‘robust’ variants, meaning that they provide robust standard errors and a scaled test statistic. For example, for the maximum likelihood estimator, lavaan provides the following robust variants:
"MLM": maximum likelihood estimation with robust standard errors and a Satorra-Bentler scaled test statistic. For complete data only.
"MLMVS": maximum likelihood estimation with robust standard errors and a mean- and variance adjusted test statistic (aka the Satterthwaite approach). For complete data only.
"MLMV": maximum likelihood estimation with robust standard errors and a mean- and variance adjusted test statistic (using a scale-shifted approach). For complete data only.
"MLF": for maximum likelihood estimation with standard errors based on the first-order derivatives, and a conventional test statistic. For both complete and incomplete data.
"MLR": maximum likelihood estimation with robust (Huber-White) standard errors and a scaled test statistic that is (asymptotically) equal to the Yuan-Bentler test statistic. For both complete and incomplete data.
ULS estimators, lavaan also provides ‘robust’
that for the robust
WLS variants, we use the diagonal of the weight matrix
for estimation, but we use the full weight matrix to correct the standard
errors and to compute the test statistic.
If maximum likelihood estimation is used (
"ML" or any of its
robusts variants), the default behavior
of lavaan is to base the analysis on the so-called biased sample
covariance matrix, where the elements are divided by $n$ instead of
$n-1$. This is done internally, and should not be done by the user. In
addition, the chi-square statistic is computed by multiplying the
minimum function value with a factor $n$ (instead of $n-1$). This is
similar to the Mplus program. If you prefer to use an unbiased
covariance, and $n-1$ as the multiplier to compute the chi-square
statistic, you need to specify the
likelihood = "wishart"
argument when calling the fitting functions. For example:
fit <- cfa(HS.model, data = HolzingerSwineford1939, likelihood = "wishart") fit
lavaan (0.6-1) converged normally after 35 iterations Number of observations 301 Estimator ML Model Fit Test Statistic 85.022 Degrees of freedom 24 P-value (Chi-square) 0.000
The value of the test statistic will be closer to the value reported by programs like EQS, LISREL or AMOS, since they all use the ‘Wishart’ approach when using the maximum likelihood estimator. The program Mplus, on the other hand, uses the ‘normal’ approach to maximum likelihood estimation.
If the data contain missing values, the default behavior is listwise deletion.
If the missing mechanism is MCAR (missing completely at random) or MAR (missing
at random), the lavaan package provides case-wise (or ‘full information’)
maximum likelihood estimation. You can turn this feature on, by using the
missing = "ML" when calling the fitting function. An unrestricted
(h1) model will automatically be estimated, so that all common fit indices are
Standard errors are (by default) based on the expected information matrix. The
only exception is when data are missing and full information ML is used (via
missing = "ML"). In this case, the observed information matrix is used to
compute the standard errors. The user can change this behavior by using the
information argument, which can be set to
If the estimator is simply
"ML", you can request robust standard errors by
se argument, which can be set to
Or simply to
"none" if you don’t
need them. This will not affect the test statistic. In fact, you can choose the
test statistic independently by using the
test argument, which can be set to
There are two ways for using the bootstrap in lavaan. Either you can set
test = "bootstrap" when fitting the model (and you will get
bootstrap standard errors, and/or a bootstrap based p-value respectively), or
you can you the
bootstrapLavaan() function, which needs an already fitted
lavaan object. The latter function can be used to ‘bootstrap’ any statistic
(or vector of statistics) that you can extract from a fitted lavaan object.