## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup, echo = FALSE, message=FALSE--------------------------------------- library(dySEM) library(dplyr) library(lavaan) DRES <- as_tibble(DRES) ## ----previewtib--------------------------------------------------------------- DRES ## ----scrape------------------------------------------------------------------- dvn <- scrapeVarCross(DRES, x_order = "sip", x_stem = "PRQC", x_delim1="_",x_delim2=".", distinguish_1="1", distinguish_2="2") ## ----configscript------------------------------------------------------------- qual.indist.script <- scriptCFA(dvn, lvname = "Quality") ## ----scriptsequence----------------------------------------------------------- qual.res.script <- scriptCFA(dvn, lvname = "Quality", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = c("none")) qual.int.script <- scriptCFA(dvn, lvname = "Quality", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = c("none")) qual.load.script <- scriptCFA(dvn, lvname = "Quality", constr_dy_meas = c("loadings"), constr_dy_struct = c("none")) qual.config.script <- scriptCFA(dvn, lvname = "Quality", constr_dy_meas = c("none"), constr_dy_struct = c("none")) ## ----modelfit, warning= FALSE------------------------------------------------- #Fit fully indistinguishable model qual.ind.fit <- lavaan::cfa(qual.indist.script, data = DRES, std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE) #Fit residual invariance model qual.res.fit <- lavaan::cfa(qual.res.script, data = DRES, std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE) #Fit intercept invariance model qual.int.fit <- lavaan::cfa(qual.int.script, data = DRES, std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE) #Fit loading invariance model qual.load.fit <- lavaan::cfa(qual.load.script, data = DRES, std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE) #Fit configural invariance model qual.config.fit <- lavaan::cfa(qual.config.script, data = DRES, std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE) ## ----summary, eval = FALSE---------------------------------------------------- # summary(qual.config.fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) ## ----anova-------------------------------------------------------------------- anova(qual.config.fit, qual.load.fit, qual.int.fit, qual.res.fit, qual.ind.fit) ## ----dyoutput, eval = FALSE--------------------------------------------------- # outputParamTab(dvn, model = "cfa", fit = qual.indist.fit, # tabletype = "measurement", writeTo = tempdir(), # fileName = "cfa_indist") # # outputParamFig(fit = qual.indist.fit, figtype = "standardized", # writeTo = tempdir(), # fileName = "cfa_indist")