In PsyChiLin/EFAshiny: EFAshiny : An User-friendly Application for Exploratory Factor Analysis

knitr::opts_chunk$set(echo = TRUE)

Overview

This tutorial aims to demonstrate how to perform the fundamental analyses in EFAshiny using R code. By following each line in this tutorial, you can learn basic R programming and can implement automated processing script in future analyses. Also, you can further use the code in EFAshiny within a script pupeline without launching the app. However, we still suggest you to use EFAshiny APP :)) You can also have a look at our tutorial of EFAshiny GUI to make an easy comparison while writing the code. Have fun with EFAshiny and R !

Preparation

First of all, we should load all the packages and fucntions in EFAshiny into R session. If you do not have these package installed, please use install.package("packagename") to perfrom installations.

require(ggplot2);require(psych);require(corrplot);require(reshape2);require(moments);require(gridExtra)
require(qgraph);require(bootnet);require(igraph);require(ggcorrplot);require(RCurl)
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/my_summary.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/faplot.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/bootEGA.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/bargraph.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/stackbar.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/printLoadings.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/theme_default.R")

Data Input

Read the demonsration data RSE into R. We assign it to a object called dta. Then using head to observe and explore this data. See our tutorial for data description.

dta <- read.csv(text=getURL("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/RSE/RSE.csv"))
head(dta)

Data Summary

Several kinds of data summary can be perform in EFAshiny. We demonstrate them using basic code. The package ggplot2 is a useful package for plotting. We use it to plot the histograms, the density plots, and the correlation matrix. You can also check the documentation of ggplot2.

Numeric Statistic

NumericStatistic <- apply(dta,2,my_summary)
row.names(NumericStatistic) <- c("Mean","SD","Skewness","Kurtosis","Median","MAD")
NumericStatistic <- as.data.frame(t(NumericStatistic))
NumericStatistic <- round(NumericStatistic,3)
NumericStatistic

Histogram

dta_long <- melt(dta)
colnames(dta_long) <- c("Item", "Response")
Histogram <- ggplot(dta_long, aes(x = Response, fill = Item))+
        geom_histogram(bins = 10)+
        facet_wrap(~Item)+
        theme_default()
Histogram

Density Plot

DensityPlot <- ggplot(dta_long, aes(x = Response, fill = Item))+
        geom_density()+
        facet_wrap(~Item)+
        theme_default()
DensityPlot 

Correlation Matrix

CorMat <- cor(as.matrix(dta))
corrplot(CorMat,order="hclust",type="upper",method="ellipse",
         tl.pos = "lt",mar = c(2,2,2,2))
corrplot(CorMat,order="hclust",type="lower",method="number",
         diag=FALSE,tl.pos="n", cl.pos="n",add=TRUE,mar = c(2,2,2,2))

ggcorplot

ggcorrplot(CorMat, hc.order = T,type = "lower", lab = TRUE,
           colors = c("#E46726", "white", "#6D9EC2"))

Factor Retention

We provide several factor retention methods in EFAshiny. Those analyses can also be performed using R code. You can adopt these methods for your own use.

Scree Plot and Parallel Analysis

PA <- faplot(CorMat,n.obs = 256, quant = 0.95)
PA[[1]]

Numeric Rules

NumericRule <- VSS(CorMat,n = 4, plot = F, n.obs = 256)
temp1 <- data.frame(nFactor = row.names(NumericRule$vss.stats), 
                     VSS1 = NumericRule$cfit.1, VSS2 = NumericRule$cfit.2, 
                     MAP = NumericRule$map)
temp2 <- NumericRule$vss.stats[,c(6:8,11)]
NumericRule <- cbind(temp1,temp2)
NumericRule

Exploratory Graph Analysis (EGA)

EGArst <- bootEGA(data = dta, n = 10, medianStructure = TRUE, plot.MedianStructure = TRUE, 
                  ncores = 4, layout = "spring")
plot(EGArst$plot)

Extarction and Rotation

The key analyses in EFA is factor extarction and rotation. We teach you to perform the analyses using psych package. Using these lines, you can obtain basic results of EFA. Can also see the code in EFAshiny server and psych package for details.

EFArst <- fa(CorMat,2,n.obs=256, rotate = "promax",fm = "pa", n.iter = 200)
EFArst

Visualization

By passing the results of EFA from aforementioned analyses into the well-established functions, you can easily visualize these results from EFA. Definitely, EFAshiny can help you to do these automatically.

Diagram

fa.diagram(EFArst,simple = T,cut = 0.33,
           sort = T,errors = T,e.size = 0.05)

Bootstrapping Factor Loadings

order <- rev(row.names(as.data.frame(printLoadings(EFArst$cis$means,sort = T,cutoff = 0)))) # define the order of the variable
bargraph(EFArst,order = order,nf = 2,highcol = "firebrick",lowcol = "chartreuse4",ci = T)

Factor Loadings and Correlation Matrix

stackbar(CorMat,EFArst,order = order,highcol = "firebrick",lowcol = "chartreuse4")

PsyChiLin/EFAshiny documentation built on May 27, 2019, 12:17 p.m.