Function Random Start Values for CFA (lavaan)

However, depending on identification method and starting values, with typical (or robust) maximum likelihood estimation (ML/MLR) analysis models may converge to suboptimal values of the likelihood function (local maxima), thereby threatening the robustness of empirical modelling and inferences about model selection. To ensure model convergence to optimal likelihood values (global maxima), Mplus offers a convenience switch to repeatedly run a model with different random start values.

Notably, open-source solutions in R lack a similarly convenient way for overcoming local maxima with the help of random start values. Here, we propose to implement R-code for reproducibe findings of the computationally optimal values for a CFA solution. While drawing on lavaan’s cfa-function (Rosseel, 2012), we use a wrapper function for calling lavaan’s cfa-function with three additional arguments.