## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(facomplex) library(lavaan) library(psych) if (!requireNamespace("psych", quietly = TRUE)) { knitr::opts_chunk$set(eval = FALSE) } if (!requireNamespace("lavaan", quietly = TRUE)) { knitr::opts_chunk$set(eval = FALSE) } ## ----------------------------------------------------------------------------- data(fullclean) ## ----------------------------------------------------------------------------- INV.target <- matrix(0, 12, 2) INV.target[1:6, 1] <- NA INV.target[7:12, 2] <- NA INV.target ## ----------------------------------------------------------------------------- INV.esem.model <- ' efa("efa1")*f1 + efa("efa1")*f2 =~ INV1 + INV4 + INV5 + INV7 + INV11 + INV12 + INV3 + INV6 + INV8 + INV9 + INV13 + INV14 ' ## ----------------------------------------------------------------------------- INV.esem.fit <- sem(INV.esem.model, data = fullclean, ordered = FALSE, estimator = "ulsmv", rotation = "target", rotation.args = list(target = INV.target, geomin.epsilon = 0.01, rstarts = 30, algorithm = "gpa", std.ov = TRUE)) ## ----------------------------------------------------------------------------- summary(INV.esem.fit, standardized = TRUE, fit.measures = TRUE) ## ----------------------------------------------------------------------------- simload(data = lavInspect(INV.esem.fit, what = "std")$lambda, items_target = list(f1 = c(1,2,3,4,5,6), f2 = c(7,8,9,10,11,12))) ## ----------------------------------------------------------------------------- Hofmann(data = lavInspect(INV.esem.fit, what = "std")$lambda) ## ----------------------------------------------------------------------------- BSI(lavInspect(INV.esem.fit, what = "std")$lambda)