## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup, message=FALSE, warning=FALSE-------------------------------------- ## load package # install.packages("equaltestMI") library(equaltestMI) ## ----setup2, message=FALSE, warning=FALSE------------------------------------- # install.packages("devtools") # library(devtools) # devtools::install_github("gabriellajg/equaltestMI", force=TRUE) library(equaltestMI) ## ----------------------------------------------------------------------------- data(LeeAlOtaiba) # contains sample covariance matrices and sample means of four groups ## ----------------------------------------------------------------------------- ## group 1 = boys ineligible for free-reduced lunches Group1 <- LeeAlOtaiba$BoysIneligible Group1 <- as.matrix(Group1) ## group 2 = boys eligible for free-reduced lunches Group2 <- LeeAlOtaiba$BoysEligible Group2 <- as.matrix(Group2) # sample means: M1 <- Group1[1,] M2 <- Group2[1,] # sample covariance matrices: Cov1 <- Group1[2:7,] Cov2 <- Group2[2:7,] ## ---- echo=FALSE-------------------------------------------------------------- M1 ## ---- echo=FALSE-------------------------------------------------------------- round(Cov1, 3) ## ---- echo=FALSE-------------------------------------------------------------- M2 ## ---- echo=FALSE-------------------------------------------------------------- round(Cov2, 3) ## ----------------------------------------------------------------------------- ## lavaan model syntax model <- ' AlphabetKnowledge =~ Letter_Name+ Letter_Sound PhonologicalAwareness =~ Blending + Elision Spelling =~ Real_Words + Pseudo_Words ' ## ---- eval=FALSE-------------------------------------------------------------- # ## the results using equivalence testing and projection method # ## full R output will be presented in Part 3 # test <- eqMI.main(model = model, # sample.nobs = c(78, 174), # sample.mean = list(M1, M2), # sample.cov = list(Cov1, Cov2), # meanstructure = TRUE, # output = 'both', # quiet = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE, bootstrap = FALSE) ## ---- echo=FALSE-------------------------------------------------------------- ## the results using equivalence testing and projection method test <- eqMI.main(model = model, sample.nobs = c(78, 174), sample.mean = list(M1, M2), sample.cov = list(Cov1, Cov2), meanstructure = TRUE, output = 'both', quiet = FALSE, equivalence.test = TRUE, adjRMSEA = TRUE, projection = TRUE, bootstrap = FALSE) ## ---- eval=FALSE-------------------------------------------------------------- # test1 <- eqMI.main(model = model, # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = FALSE, adjRMSEA = FALSE) ## ---- eval=FALSE-------------------------------------------------------------- # test2 <- eqMI.main(model = model, # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = FALSE, adjRMSEA = FALSE, # projection = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test3 <- eqMI.main(model = model, # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = FALSE) ## ---- eval=FALSE-------------------------------------------------------------- # test4 <- eqMI.main(model = model, # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test5 <- eqMI.main(model = model, # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test6 <- eqMI.main(model = model, structure = 'mean', # sample.nobs = c(78, 174), sample.cov = list(Cov1, Cov2), # sample.mean = list(M1, M2), meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test7 <- eqMI.main(model = model, data = literacy.dat, # group = "FRL", meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test8 <- eqMI.main(model = model, data = literacy.dat, # group = "FRL", meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE, bootstrap = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test9 <- eqMI.main(model = model, data = literacy.dat, # group = "FRL", meanstructure = TRUE, # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE, bootstrap = FALSE, # quite = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # test10 <- eqMI.main(model = model, data = literacy.dat, # group = "FRL", meanstructure = TRUE, # group.partial = c("Spelling=~Real_Words", "Blending~1"), # equivalence.test = TRUE, adjRMSEA = TRUE, # projection = TRUE) ## ---- echo=FALSE-------------------------------------------------------------- library(printr) ?eqMI.main ## ---- include=FALSE----------------------------------------------------------- # For a complete view of the help page of function eqMI.main(), please install R package printr and type ?eqMI.main in R console: #library(printr) #?eqMI.main #output: pdf_document #devtools::build_vignettes() #R CMD Rd2pdf "~/Box Sync/MacSync/Research/Frontier/equaltestMI" #R CMD check --as-cran equaltestMI_0.6.0.tar.gz #https://bookdown.org/yihui/rmarkdown-cookbook/package-vignette.html