--- title: "EXAMPLES" author: "Brian O'Connor" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 number_sections: false vignette: > %\VignetteIndexEntry{EXAMPLES} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ```{r, include = FALSE} knitr::opts_chunk$set( collapse = FALSE, comment = "", fig.width = 7, fig.align = "center", background='#FFFFFF' ) ``` ```{css, echo=FALSE} h1 { font-size: 30px; color: black !important; } h2 { font-size: 20px; color: blue !important; } #TOC { position: fixed; left: 10px; top: 10px; width: 200px; height: 100%; overflow:auto; } #TOC::before { content: ""; } #TOC li { list-style-type: none; } #TOC li { margin-bottom: 10px; } /* Change color of all links inside the TOC */ #TOC a { color: blue; /* Change to your preferred hex color */ } /* Target Level 1 Headings (H1) in the TOC */ #TOC > ul > li > a { color: black; /* Vibrant Red */ font-weight: bold; } /* Target Level 2 Headings (H2) in the TOC */ #TOC > ul > li > ul > li > a { color: blue; /* Slate Blue */ } /* Change color of the hovered/active TOC link */ #TOC a:hover, #TOC .active { color: #D0021B !important; background-color: #F5F5F5; /* Background color of active item */ } body { max-width: 800px; margin: auto; margin-left:230px; line-height: 20px; } ``` This R markdown document provides R code and output for multiple functions and datasets. ## Load the EFA.dimensions package ```{r } library(EFA.dimensions) ``` # ESEM examples: ## ESEM, 'efa_blocks', no target, Holzinger & Swineford 1939 data ```{r collapse=FALSE} ESEM(data = data_HS_1939[,-10], method = 'efa_blocks', Nfactors = 3) ``` ## ESEM, 'startvalues', no target, Holzinger & Swineford 1939 data ```{r collapse=FALSE} ESEM(data = data_HS_1939[,-10], method = 'startvalues', Nfactors = 3) ``` ## ESEM, 'efa_blocks', with a target, Holzinger & Swineford 1939 data ```{r collapse=FALSE} ESEM(data = data_HS_1939[,-10], method = 'efa_blocks', target_keys = c(1,1,1,2,2,2,3,3,3)) ``` ## ESEM, 'startvalues', with a target, Holzinger & Swineford 1939 data ```{r collapse=FALSE} ESEM(data = data_HS_1939[,-10], method = 'startvalues', target_keys = c(1,1,1,2,2,2,3,3,3)) ``` ## ESEM, 'efa_blocks', with a target, Sanchez-Oliva et al. (2017) data ```{r collapse=FALSE} ESEM(data = data_SDT, method = 'efa_blocks', target_keys = c(1,1,1,1,2,2,2,2,3,3,3,3)) ``` ## ESEM, 'efa_blocks', MPlus 4.1b data ```{r collapse=FALSE} MPlus_4.1b_data <- read.table("https://www.statmodel.com/usersguide/chap4/ex4.1b.dat", header=FALSE) colnames(MPlus_4.1b_data) <- paste0("y", 1:ncol(MPlus_4.1b_data)) ESEM(data = MPlus_4.1b_data, method = 'efa_blocks', Nfactors = 4, estimator = 'ML') # compare with the MPlus output at # https://www.statmodel.com/usersguide/chap4/ex4.1part2.html ``` # BIFACTOR examples: ## BIFACTOR using ESEM, Holzinger & Swineford 1939 data ```{r collapse=FALSE} BIFACTOR(rawdata = data_HS_1939[,-10], bifactor_kind = 'ESEM', Nfactors = 4, LV_options = list(group_keys = NULL, estimator = 'MLR', rotation = 'bigeomin', resid_correls = NULL, LV_names = NULL, ordered = FALSE)) ``` ## BIFACTOR using CFA, Sanchez-Oliva et al. (2017) data ```{r collapse=FALSE} BIFACTOR(rawdata = data_SDT, bifactor_kind = 'CFA', LV_options = list(group_keys = c(1,1,1,1,2,2,2,2,3,3,3,3), estimator='MLR', resid_correls=NULL, LV_names=NULL, ordered = FALSE)) ``` ## BIFACTOR using ESEM, Sanchez-Oliva et al. (2017) data ```{r collapse=FALSE} BIFACTOR(rawdata = data_SDT, bifactor_kind = 'ESEM', EFA_options = list(extraction = 'minres', rotation = 'oblimin', Nfactors = 3), LV_options = list(group_keys = c(1,1,1,1,2,2,2,2,3,3,3,3), estimator='MLR', resid_correls=NULL, LV_names=NULL, ordered = FALSE)) ``` ## BIFACTOR using ESEM, MPlus 4.7 data ```{r collapse=FALSE} MPlus_4.7_data <- read.table("https://www.statmodel.com/usersguide/chap4/ex4.7.dat", header=FALSE) colnames(MPlus_4.7_data) <- paste0("y", 1:ncol(MPlus_4.7_data)) BIFACTOR(rawdata = MPlus_4.7_data, Nfactors = 3, bifactor_kind = 'ESEM', LV_options = list(group_keys = NULL, estimator='ML', rotation = 'bigeomin', resid_correls=NULL, LV_names=NULL, ordered = FALSE)) # compare with the MPlus output at # hhttps://www.statmodel.com/usersguide/chap4/ex4.7.html ``` # Factorial_Invariance examples: ## using LV_model, Holzinger & Swineford 1939 data ```{r collapse=FALSE} HS_1939_model <- 'visual =~ test1 + test2 + test3 textual =~ test4 + test5 + test6 speed =~ test7 + test8 + test9' HS_1939_output <- Factorial_Invariance(data = data_HS_1939, group = 'school', LV_model = HS_1939_model) PLOT_Invariance(model_object = HS_1939_output, invar_model = 'Scalar', plot_types = c('loadings', 'LV_distribs')) # for more detailed statistical comparisons of the latent variable means, try # the GROUP.DIFFS function from the DFA.CANCOR package, as follows: # install.packages(DFA.CANCOR); library(DFA.CANCOR) # # LV_scores <- HS_1939_output$LV_scores # # GROUP.DIFFS(data = LV_scores, GROUPS = 'school', DV = 'visual') # # GROUP.DIFFS(data = LV_scores, GROUPS = 'school', DV = 'speed') ``` ## using LV_keys, Holzinger & Swineford 1939 data ```{r collapse=FALSE} HS_1939_output <- Factorial_Invariance(data = data_HS_1939, group = 'school', LV_keys = c(1,1,1, 2,2,2, 3,3,3), LV_names = c('visual', 'textual', 'speed')) PLOT_Invariance(model_object = HS_1939_output, invar_model = 'Configural', plot_types = c('loadings', 'ints_slopes')) ``` ## another way of using LV_keys, Holzinger & Swineford 1939 data ```{r collapse=FALSE} Factorial_Invariance(data = data_HS_1939, group = 'school', LV_keys = c(test1 = 1, test2 = 1, test3 = 1, test4 = 2, test5 = 2, test6 = 2, test7 = 3, test8 = 3, test9 = 3) ) ``` ## with a correlated residuals term, Brown 2015 data ```{r collapse=FALSE} # 2015 Brown - Confirmatory Factor Analysis for Applied Research p 247, Table 7.11 # Tests of measurement invariance and population heterogeneity of DSM-IV # major depressive disorder in men and women data_Brown_2015 <- read.table("http://people.bu.edu/tabrown/Ch7/MDDALL.dat") names(data_Brown_2015) <- c("sex", paste("mdd", 1:9, sep = "")) data_Brown_2015$sex <- factor(data_Brown_2015$sex, levels = c(0, 1), labels = c("female", "male")) # using LV_model model.mdd <- ' MDD =~ mdd1 + mdd2 + mdd3 + mdd4 + mdd5 + mdd6 + mdd7 + mdd8 + mdd9 mdd1 ~~ mdd2 ' Factorial_Invariance(data = data_Brown_2015, group = 'sex', LV_model = model.mdd) # using LV_keys Brown_2015_output <- Factorial_Invariance(data = data_Brown_2015, group = 'sex', LV_keys = c(1,1,1,1,1,1,1,1,1), LV_resid_correls = c('mdd1 ~~ mdd2')) PLOT_Invariance(model_object = Brown_2015_output, invar_model = 'Scalar', plot_types = c('loadings', 'LV_distribs')) ```