estfun: Extract Empirical Estimating Functions

estfunR Documentation

Extract Empirical Estimating Functions


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)



An object of class lavaan.


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.


Ed Merkle; the remove.duplicated, ignore.constraints and remove.empty.cases arguments were added by Yves Rosseel

lavaan documentation built on Jan. 9, 2023, 9:05 a.m.