Note, the loading on ind60 -> x1 is fixed to 1 for identifiability by common convention. In this model, the model is fit to the covariance matrix of the data. Think of ind60 as causing, i.e., being a predictor of, all of its indicators.
Moreover, lavPredict can generate predicted factor scores, for example. In full SEMs, latent variables are later used just like any other variable - as predictors, as responses, etc.
Would it be possible to incorporate factor analysis or latent variables in general into brms?
In a structural equation modeling framework, we often define latent unmeasured variables with indicators. For example
produces loadings and variances
Note, the loading on ind60 -> x1 is fixed to 1 for identifiability by common convention. In this model, the model is fit to the covariance matrix of the data. Think of ind60 as causing, i.e., being a predictor of, all of its indicators.
Moreover,
lavPredict
can generate predicted factor scores, for example. In full SEMs, latent variables are later used just like any other variable - as predictors, as responses, etc.Would it be possible to incorporate factor analysis or latent variables in general into brms?