Generates an EFA model to be used by lavaan and regsem Function created by Florian Scharf for the paper Should regularization replace simple structure rotation in Exploratory Factor Analysis – Scharf & Nestler (in press at SEM)
Number of latent factors to generate.
Names of variables to be used as indicators
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## Not run: HS <- data.frame(scale(HolzingerSwineford1939[,7:15])) # Note to find number of factors, recommended to use # fa.parallel() from the psych package # using the wrong number of factors can distort the results mod = efaModel(3, colnames(HS)) semFit = sem(mod, data = HS, int.ov.free = FALSE, int.lv.free = FALSE, std.lv = TRUE, std.ov = TRUE, auto.fix.single = FALSE, se = "none") # note it requires smaller penalties than other applications reg.out2 = cv_regsem(model = semFit, pars_pen = "loadings", mult.start = TRUE, multi.iter = 10, n.lambda = 100, type = "lasso", jump = 10^-5, lambda.start = 0.001) reg.out2 plot(reg.out2) # note that the solution jumps around -- make sure best fit makes sense ## End(Not run)
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