A function for extracting the empirical estimating functions of a fitted lavaan model. This is the derivative of the objective function with respect to the parameter vector, evaluated at the observed (case-wise) data. In other words, this function returns the case-wise scores, evaluated at the fitted model parameters.
estfun.lavaan(object, scaling = FALSE, ignore.constraints = FALSE,
remove.duplicated = TRUE, remove.empty.cases = TRUE)
lavScores(object, scaling = FALSE, ignore.constraints = FALSE,
remove.duplicated = TRUE, remove.empty.cases = TRUE)If TRUE, the scores are scaled to reflect the specific
objective function used by lavaan. If FALSE (the default), the
objective function is the loglikelihood function assuming multivariate
normality.
Logical. If TRUE, the scores do not reflect
the (equality or inequality) constraints. If FALSE, the scores are
computed by taking the unconstrained scores, and adding the term
t(R) lambda, where lambda are the (case-wise) Lagrange
Multipliers, and R is
the Jacobian of the constraint function. Only in the latter case will
the sum of the columns be (almost) equal to zero.
If TRUE, and all the equality constraints have
a simple form (eg. a == b), the unconstrained scores are post-multiplied with a
transformation matrix in order to remove the duplicated parameters.
If TRUE, empty cases with only missing values
will be removed from the output.
A n x k matrix corresponding to n observations and k parameters.