Blcfa: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis. Issue 4 (4th July 2021) - Zhang, Lijin; Pan, Junhao; Dubé, Laurette; Ip, Edward Haksing
ABSTRACT: In confirmatory factor analysis (CFA), post hoc model modification (PMM) indexes are often used to adjust for possible residual correlations between items. Although the approach is useful for improving model goodness-of-fit, it requires an iterative, one-item-pair-at-a-time procedure that can be tedious and prone to error. This paper provides a didactic discussion in the form of a tutorial of a more efficient and practical alternative and its implementation using an R-based package. The tutorial contains (1) the Bayesian covariance Lasso (least absolute shrinkage and selection operator) approach as an alternative to the PMM method, and (2) the R package blcfa, which implements the Bayesian covariance lasso and directly interfaces with M plus . It adopts a two-step approach by first estimating the entire residual covariance matrix, and then identifying the nonzero entries and seamlessly feeding them into M plus . Two examples were used to illustrate package implementation.