library(shiny)
library(shinyAce)
# ANOVAベースのG理論計算
shinyServer(function(input, output) {
# p × i デザインの分散成分推定
estimate_var_pi <- function(dat) {
dat$Student <- factor(dat$Student)
dat$Item <- factor(dat$Item)
np <- length(levels(dat$Student))
ni <- length(levels(dat$Item))
aov_result <- aov(Score ~ Student + Item, data = dat)
aov_summary <- summary(aov_result)[[1]]
# 行名のスペースを除去
rownames(aov_summary) <- trimws(rownames(aov_summary))
MS_p <- aov_summary["Student", "Mean Sq"]
MS_i <- aov_summary["Item", "Mean Sq"]
MS_res <- aov_summary["Residuals", "Mean Sq"]
var_p <- max(0, (MS_p - MS_res) / ni)
var_i <- max(0, (MS_i - MS_res) / np)
var_res <- MS_res
list(Student = var_p, Item = var_i, Residual = var_res,
counts = list(Item = ni), np = np, ni = ni)
}
# p × r × i デザインの分散成分推定
estimate_var_pri <- function(dat) {
dat$Student <- factor(dat$Student)
dat$Rater <- factor(dat$Rater)
dat$Item <- factor(dat$Item)
np <- length(levels(dat$Student))
nr <- length(levels(dat$Rater))
ni <- length(levels(dat$Item))
aov_result <- aov(Score ~ Student * Rater * Item, data = dat)
aov_summary <- summary(aov_result)[[1]]
# 行名のスペースを除去
rownames(aov_summary) <- trimws(rownames(aov_summary))
MS_p <- aov_summary["Student", "Mean Sq"]
MS_r <- aov_summary["Rater", "Mean Sq"]
MS_i <- aov_summary["Item", "Mean Sq"]
MS_pr <- aov_summary["Student:Rater", "Mean Sq"]
MS_pi <- aov_summary["Student:Item", "Mean Sq"]
MS_ri <- aov_summary["Rater:Item", "Mean Sq"]
# 三元交互作用を残差として使用
MS_res <- aov_summary["Student:Rater:Item", "Mean Sq"]
var_p <- max(0, (MS_p - MS_pr - MS_pi + MS_res) / (nr * ni))
var_r <- max(0, (MS_r - MS_pr - MS_ri + MS_res) / (np * ni))
var_i <- max(0, (MS_i - MS_pi - MS_ri + MS_res) / (np * nr))
var_pr <- max(0, (MS_pr - MS_res) / ni)
var_pi <- max(0, (MS_pi - MS_res) / nr)
var_ri <- max(0, (MS_ri - MS_res) / np)
var_res <- MS_res
list(Student = var_p, Rater = var_r, Item = var_i,
"Student:Rater" = var_pr, "Student:Item" = var_pi, "Rater:Item" = var_ri,
Residual = var_res, counts = list(Rater = nr, Item = ni),
np = np, nr = nr, ni = ni)
}
var.est <- reactive({
dat <- read.csv(text=input$text, sep="\t")
if (input$type == "pi") {
dat$Student <- factor(rownames(dat))
col.n <- length(dat)-1
dat <- reshape(dat, idvar="Student", varying=2:col.n, v.names="Score", direction="long")
dat <- dat[,-1]
colnames(dat) <- c("Student", "Item", "Score")
vc <- estimate_var_pi(dat)
compOut <- c(vc$Student, vc$Item, vc$Residual)
lbl <- c("Student", "Item", "Residual")
} else {
vc <- estimate_var_pri(dat)
compOut <- c(vc$Student, vc$Rater, vc$Item,
vc[["Student:Rater"]], vc[["Student:Item"]], vc[["Rater:Item"]],
vc$Residual)
lbl <- c("Student", "Rater", "Item", "Student*Rater", "Student*Item", "Rater*Item", "Residual")
}
compTbl <- data.frame(VarComp = round(compOut, 3), Percent = round(compOut/sum(compOut) * 100, 1))
rownames(compTbl) <- lbl
colnames(compTbl) <- c("VarComp", "%")
compTblTemp <- compTbl[1:(nrow(compTbl)-1),]
compTblTemp <- compTblTemp[order(compTblTemp[,1], decreasing=T),]
compTblOrderd <- rbind(compTblTemp, compTbl[nrow(compTbl),])
cat("Variance components\n\n")
print(compTbl)
cat("\nVariance components (Sorted)\n\n")
print(compTblOrderd)
})
g.coef <- reactive({
dat <- read.csv(text=input$text, sep="\t")
if (input$type == "pi") {
dat$Student <- factor(rownames(dat))
col.n <- length(dat)-1
dat <- reshape(dat, idvar="Student", varying=2:col.n, v.names="Score", direction="long")
dat <- dat[,-1]
colnames(dat) <- c("Student", "Item", "Score")
vc <- estimate_var_pi(dat)
ni <- vc$ni
gcoeff <- vc$Student / (vc$Student + vc$Residual/ni)
} else {
vc <- estimate_var_pri(dat)
nr <- vc$nr
ni <- vc$ni
gcoeff <- vc$Student / (vc$Student + vc[["Student:Rater"]]/nr + vc[["Student:Item"]]/ni + vc$Residual/(nr*ni))
}
cat("G =", sprintf("%.3f", round(gcoeff, 3)))
})
phi <- reactive({
dat <- read.csv(text=input$text, sep="\t")
if (input$type == "pi") {
dat$Student <- factor(rownames(dat))
col.n <- length(dat)-1
dat <- reshape(dat, idvar="Student", varying=2:col.n, v.names="Score", direction="long")
dat <- dat[,-1]
colnames(dat) <- c("Student", "Item", "Score")
vc <- estimate_var_pi(dat)
ni <- vc$ni
phi_val <- vc$Student / (vc$Student + vc$Item/ni + vc$Residual/ni)
} else {
vc <- estimate_var_pri(dat)
nr <- vc$nr
ni <- vc$ni
phi_val <- vc$Student / (vc$Student + vc$Rater/nr + vc$Item/ni +
vc[["Student:Rater"]]/nr + vc[["Student:Item"]]/ni +
vc[["Rater:Item"]]/(nr*ni) + vc$Residual/(nr*ni))
}
cat("Phi =", sprintf("%.3f", round(phi_val, 3)))
})
D <- reactive({
dat <- read.csv(text=input$text, sep="\t")
if (input$type == "pi") {
dat$Student <- factor(rownames(dat))
col.n <- length(dat)-1
dat <- reshape(dat, idvar="Student", varying=2:col.n, v.names="Score", direction="long")
dat <- dat[,-1]
colnames(dat) <- c("Student", "Item", "Score")
vc <- estimate_var_pi(dat)
n.items <- input$n.items
ival <- 1:n.items
gvals <- sapply(ival, function(ni) vc$Student / (vc$Student + vc$Residual/ni))
cat("G-coefficients\n\n")
result <- data.frame(G = sprintf("%.3f", round(gvals, 3)))
rownames(result) <- paste("Item =", ival)
print(result)
} else {
vc <- estimate_var_pri(dat)
n.raters <- input$n.raters
n.items <- input$n.items
ival <- 1:n.raters
jval <- 1:n.items
plotValsG <- matrix(nrow=length(ival), ncol=length(jval))
for(i in 1:length(ival)) {
for(j in 1:length(jval)) {
nr <- ival[i]
ni <- jval[j]
plotValsG[i,j] <- vc$Student / (vc$Student + vc[["Student:Rater"]]/nr + vc[["Student:Item"]]/ni + vc$Residual/(nr*ni))
}
}
cat("G-coefficients (row: Rater, column: Item)\n\n")
gvals <- data.frame(matrix(sprintf("%.3f", round(plotValsG, 3)), ncol=ncol(plotValsG)))
colnames(gvals) <- jval
rownames(gvals) <- paste("Rater =", ival)
print(gvals)
}
})
makePlot <- function(){
dat <- read.csv(text=input$text, sep="\t")
if (input$type == "pi") {
dat$Student <- factor(rownames(dat))
col.n <- length(dat)-1
dat <- reshape(dat, idvar="Student", varying=2:col.n, v.names="Score", direction="long")
dat <- dat[,-1]
colnames(dat) <- c("Student", "Item", "Score")
vc <- estimate_var_pi(dat)
n.items <- input$n.items
ival <- 1:n.items
plotValsG <- sapply(ival, function(ni) vc$Student / (vc$Student + vc$Residual/ni))
plot(ival, plotValsG, xlim=c(1, max(ival)), ylim=c(0, 1), type="b", pch=1, col=1, lty=1,
xlab="Items", ylab="G-coefficients", main="D Study: G-coefficients by Number of Items")
abline(h=0.8, lty=2, col="gray")
} else {
vc <- estimate_var_pri(dat)
n.raters <- input$n.raters
n.items <- input$n.items
ival <- 1:n.raters
jval <- 1:n.items
plotValsG <- matrix(nrow=length(ival), ncol=length(jval))
for(i in 1:length(ival)) {
for(j in 1:length(jval)) {
nr <- ival[i]
ni <- jval[j]
plotValsG[i,j] <- vc$Student / (vc$Student + vc[["Student:Rater"]]/nr + vc[["Student:Item"]]/ni + vc$Residual/(nr*ni))
}
}
lbls <- paste("Rater =", ival)
plot(c(0,0), xlim=c(1, max(jval)), ylim=c(0, 1), type="n",
xlab="Items", ylab="G-coefficients", main="D Study")
abline(h=0.8, lty=2, col="gray")
legend("bottomright", cex=0.7, legend=lbls, lty=1:n.raters, pch=1:n.raters, col=1:n.raters)
for(i in 1:nrow(plotValsG)) {
points(jval, plotValsG[i,], pch=i, col=i)
lines(jval, plotValsG[i,], col=i, lty=i)
}
}
}
output$Plot <- renderPlot({ makePlot() })
info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
info2 <- paste("It was executed on ", date(), ".", sep = "")
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$info.out <- renderPrint({ info() })
output$var.est.out <- renderPrint({ var.est() })
output$g.coef.out <- renderPrint({ g.coef() })
output$phi.out <- renderPrint({ phi() })
output$D.out <- renderPrint({ D() })
})
library(shiny)
library(shinyAce)
shinyUI(bootstrapPage(
headerPanel("Generalizability Theory"),
sidebarPanel(
radioButtons("type", strong("Design (all crossed):"),
list("p × i" = "pi",
"p × r × i" = "pri") , selected = "pri"
),
br()
),
mainPanel(
tabsetPanel(
tabPanel("Main",
p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),
p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have exactly the same header (variable names) in the first row.</div></b>")),
br(),
aceEditor("text", 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mode="r", theme="cobalt"),
br(),
h3("Variance components"),
verbatimTextOutput("var.est.out"),
br(),
h3("G-coefficient"),
verbatimTextOutput("g.coef.out"),
br(),
h3("Phi"),
verbatimTextOutput("phi.out"),
br(),
h3("D study"),
# Display this only if "pri" is checked
conditionalPanel(condition = "input.type == 'pri'",
numericInput("n.raters", "Number of raters", 3)
),
numericInput("n.items", "Number of items", 5),
verbatimTextOutput("D.out"),
plotOutput("Plot", height = "550px"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("info.out")
),
tabPanel("Input Examples",
p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),
p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have exactly the same header (variable names) in the first row.</div></b>")),
br(),
p(strong("p × i Design")),
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p(strong("p × r × i Design")),
aceEditor("text2", 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mode="r", theme="solarized_light"),
br()
),
tabPanel("About",
strong('Note'),
p('This web application is developed with',
a("Shiny.", href="http://www.rstudio.com/shiny/", target="_blank"),
''),
br(),
strong('List of Packages Used'), br(),
code('library(shiny)'),br(),
code('library(shinyAce)'),br(),
code('library(lme4)'),br(),
br(),
strong('Code'),
p('Source code for this application is based on',
a('"The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012)', href='http://mizumot.com/handbook/', target="_blank"), 'and',
a("MacR.", href="https://sites.google.com/site/casualmacr/", target="_blank")),
p('The code for this web application is available at',
a('GitHub.', href='https://github.com/mizumot/g-theory', target="_blank")),
p('If you want to run this code on your computer (in a local R session), run the code below:',
br(),
code('library(shiny)'),br(),
code('runGitHub("g-theory","mizumot")')
),
br(),
strong('Citation in Publications'),
p('Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp'),
br(),
strong('Article'),
p('Mizumoto, A., & Plonsky, L. (2015).', a("R as a lingua franca: Advantages of using R for quantitative research in applied linguistics.", href='http://applij.oxfordjournals.org/content/early/2015/06/24/applin.amv025.abstract', target="_blank"), em('Applied Linguistics,'), 'Advance online publication. doi:10.1093/applin/amv025'),
br(),
strong('Recommended'),
p('To learn more about R, I suggest this excellent and free e-book (pdf),',
a("A Guide to Doing Statistics in Second Language Research Using R,", href="http://cw.routledge.com/textbooks/9780805861853/guide-to-R.asp", target="_blank"),
'written by Dr. Jenifer Larson-Hall.'),
p('Also, if you are a cool Mac user and want to use R with GUI,',
a("MacR", href="https://sites.google.com/site/casualmacr/", target="_blank"),
'is defenitely the way to go!'),
br(),
strong('Author'),
p(a("Atsushi MIZUMOTO,", href="http://mizumot.com", target="_blank"),' Ph.D.',br(),
'Professor of Applied Linguistics',br(),
'Faculty of Foreign Language Studies /',br(),
'Graduate School of Foreign Language Education and Research,',br(),
'Kansai University, Osaka, Japan'),
br(),
a(img(src="http://i.creativecommons.org/p/mark/1.0/80x15.png"), target="_blank", href="http://creativecommons.org/publicdomain/mark/1.0/"),
p(br())
)
)
)
))
