LAWBL is to provide a variety of models to analyze latent variables based on Bayesian learning. For more information about the package, one can see here or here.
How to use this package in brief
- A design matrix Q is needed for PCFA, GPCFA, or PCIRM, but not necessary for PEFA
- Default setting can be used to minimize input (e.g., burn-in, formal iteration, maximum number of factors)
- To estimate PCFA-LI (when only a few loadings can be specified, e.g., 2 per factor), use m <- pcfa(dat=dat,Q=Q,LD=F)
- To estimate PCFA (with one specified loading per item), use m <- pcfa(dat=dat,Q=Q,LD=T)
- To estimate BREFA or FEFA (i.e., PFEA without partial information), use m <- pefa(dat=dat)
- To summarize basic information after estimation, use summary(m)
- To summarize significant loadings in pattern/Q-matrix format, use summary(m,what=‘qlambda’)
- To summarize factorial eigenvalues, use summary(m,what=‘eigen’)
- To summarize significant LD terms, use summary(m,what=‘offpsx’)
- To plot eigenvalues’ trace, use plot_lawbl(m)
- To plot eigenvalues’ density, use plot_lawbl(m, what=‘density’)
- To plot eigenvalues’ adjusted PSRF, use plot_lawbl(m, what=‘APSR’)
You are also encouraged to visit here for an online reference of all functions.
For examples of how to use the package, see
- For PCFA with continuous data: here
- For GPCFA with categorical and mixed-type data: here
- For PCIRM with dichotomous data and intercept terms: here
If you would like to contribute an example to this website, please send your .Rmd file to me at email@example.com.