library(shiny)
library(shinyAce)
library(psych)
library(beeswarm)
shinyServer(function(input, output) {
#-----------------------------------------------------------------
# Mann-Whitney U-test (Comparing two independent conditions)
#-----------------------------------------------------------------
# Basic statistics
MWU.bs <- reactive({
dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))
describeBy(dat[,2], dat[,1])
})
output$MWU.bs.out <- renderPrint({
MWU.bs()
})
# Rank
MWU.ranking <- reactive({
dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))
ranked <- rank(dat[,2])
data <- data.frame(dat[,1], ranked)
n <- round(tapply(data[,2], data[,1], length),2)
m <- round(tapply(data[,2], data[,1], mean),2)
t <- round(tapply(data[,2], data[,1], sum),2)
ranks <- data.frame(n, m, t)
colnames(ranks) <- c("n","Rank Mean","Rank Sum")
print(ranks)
})
output$MWU.ranking.out <- renderPrint({
MWU.ranking()
})
# Box plot
MWU.boxPlot <- function(){
dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))
boxplot(dat[,2] ~ dat[,1], las=1)
beeswarm(dat[,2] ~ dat[,1], col = 4, pch = 16, vert = TRUE, add = TRUE)
}
output$MWU.boxPlot <- renderPlot({
print(MWU.boxPlot())
})
# Mann-Whitney U-test
MWU.test <- reactive({
dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))
dat2 <- split(dat, dat[,1])
x <- dat2[[1]][,2]
y <- dat2[[2]][,2]
max.len = max(length(x), length(y))
x <- c(x, rep(NA, max.len - length(x)))
y <- c(y, rep(NA, max.len - length(y)))
U.test <- function(x, y, correct = TRUE)
{
x <- x[!is.na(x)]
y <- y[!is.na(y)]
n1 <- length(x)
n2 <- length(y)
n <- n1 + n2
xy <- c(x, y)
r <- rank(xy)
U1 <- n1*n2+n1*(n1+1)/2-sum(r[1:n1])
tie <- table(r)
U <- min(U1, n1*n2-U1) # U
V <- n1*n2*(n^3-n-sum(tie^3-tie))/12/(n^2-n) # variance ties considered
E <- n1*n2/2 # Expected
z <- ((U-E)-ifelse(correct, 0.5, 0))/sqrt(V) # z-value
EffectSize.r <- abs(z)/sqrt(n)
ESrCI <- r.con(EffectSize.r, n, p =.95, twotailed=TRUE)
P <- pnorm(abs(z), lower.tail=FALSE)*2
cat(" Mann-Whitney U-test", "\n",
"\n",
"U =", U, ",", "E(U) =", E, ",", "V(U) =", V, "\n",
"z-value =", round(z, 3), "\n",
"p-value =", P, "\n", "\n",
"Effect size r [95% CI]=", round(EffectSize.r, 3), "[", round(ESrCI, 3), "]", "\n"
)
}
U.test(x, y, correct = FALSE)
})
output$MWU.test.out <- renderPrint({
MWU.test()
})
# Info
MWU.info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
info2 <- paste("It was executed on ", date(), ".", sep = "")
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$MWU.info.out <- renderPrint({
MWU.info()
})
#-----------------------------------------------------------------
# Wilcoxon signed-rank test (Comparing two related conditions)
#-----------------------------------------------------------------
# Basic statistics
WSR.bs <- reactive({
dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))
describe(dat)
})
output$WSR.bs.out <- renderPrint({
WSR.bs()
})
# Rank
WSR.ranking <- reactive({
dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))
dat$diff <- dat[,2] - dat[,1]
dat$sign <- ifelse(dat$diff < 0, "Negative", ifelse((dat$diff > 0), "Positive", "Tie"))
newdata <- subset(dat, dat$sign != "Tie") # Except Tie
newdata$rank <- rank(abs(newdata$diff))
n <- tapply(dat[,1], dat$sign, length)
m <- tapply(newdata$rank, newdata$sign, mean)
t <- tapply(newdata$rank, newdata$sign, sum)
list(n = n, "Rank Mean" = round(m, 2), "Rank Sum" = round(t, 2))
})
output$WSR.ranking.out <- renderPrint({
WSR.ranking()
})
# Box plot
WSR.boxPlot <- function(){
dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))
boxplot(dat, las=1)
beeswarm(dat, col = 4, pch = 16, vert = TRUE, add = TRUE)
}
output$WSR.boxPlot <- renderPlot({
print(WSR.boxPlot())
})
# Wilcoxon signed-rank test
WSR.test <- reactive({
dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))
x <- dat[,1]
x <- x[!is.na(x)]
y <- dat[,2]
y <- y[!is.na(y)]
options(warn=-1) # Suppress a warning message
result <- wilcox.test(x, y, paired=TRUE, correct=FALSE)
pval <- result$p.value
z <- qnorm(1-(pval/2))
r1 <- z/sqrt(length(x)*2)
esR.CI1 <- r.con(r1, length(x*2), p =.95, twotailed=TRUE)
#r2 <- z/sqrt(length(x)-sum((y-x==0)))
#esR.CI2 <- round(r.con(r2, length(x)-sum((y-x==0)), p =.95, twotailed=TRUE), 3)
print(result)
cat(" z-value =", round(z, 3), "\n",
"\n",
"Effect size r [95% CI] =", round(r1, 3), "[", round(esR.CI1, 3), "]", "\n"
)
#"Effect size r (without considering ties) [95% CI]=", round(r2, 3), "[", esR.CI1, "]", "\n")
})
output$WSR.test.out <- renderPrint({
WSR.test()
})
# Info
WSR.info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
info2 <- paste("It was executed on ", date(), ".", sep = "")
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$WSR.info.out <- renderPrint({
WSR.info()
})
#-----------------------------------------------------------------
# Kruskal-Wallis test (Differences between several independent groups)
#-----------------------------------------------------------------
# Basic statistics
KW.bs <- reactive({
dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))
describeBy(dat[,2], dat[,1])
})
output$KW.bs.out <- renderPrint({
KW.bs()
})
# Rank
KW.ranking <- reactive({
dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))
ranked <- rank(dat[,2])
data <- data.frame(dat[,1], ranked)
n <- round(tapply(data[,2], data[,1], length),2)
m <- round(tapply(data[,2], data[,1], mean),2)
t <- round(tapply(data[,2], data[,1], sum),2)
ranks <- data.frame(n, m, t)
colnames(ranks) <- c("n","Rank Mean","Rank Sum")
print(ranks)
})
output$KW.ranking.out <- renderPrint({
KW.ranking()
})
# Box plot
KW.boxPlot <- function(){
dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))
boxplot(dat[,2] ~ dat[,1], las=1)
beeswarm(dat[,2] ~ dat[,1], col = 4, pch = 16, vert = TRUE, add = TRUE)
}
output$KW.boxPlot <- renderPlot({
print(KW.boxPlot())
})
# Kruskal-Wallis test
KW.test <- reactive({
dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))
result <- kruskal.test(dat[,2] ~ dat[,1])
print(result)
z <- qnorm(1 - (result$p.value/2)) # p to z
esR <- abs(z)/sqrt(nrow(dat)) # z to r
# 95%CI of r
esR.CI <- r.con(esR, nrow(dat), p =.95, twotailed=TRUE)
cat("Effect size r [95% CI] =", round(esR, 3), "[", round(esR.CI, 3), "]", "\n")
cat(" *Converted from p-value (p -> z -> r)", "\n", "\n", "\n")
#eta2 <- result$statistic[[1]]/(length(dat[,1])-1)
#cat("Effect size (eta-squared) =", sprintf("%.3f",round(eta2,4)), "\n")
#cat("*Kruskal-Wallis chi-squared / (sample size - 1)", "\n", "\n")
# pair-wise comparisons
cat("=============================================================", "\n")
cat("\n", "Pairwise comparisons (Test statistics and effect sizes)", "\n", "\n")
cat("=============================================================", "\n", "\n")
U.test <- function(x, y, correct = TRUE) # this is used in "pairWiseU"
{
x <- x[!is.na(x)]
y <- y[!is.na(y)]
n1 <- length(x)
n2 <- length(y)
n <- n1+n2
xy <- c(x, y)
r <- rank(xy)
U1 <- n1*n2+n1*(n1+1)/2-sum(r[1:n1])
tie <- table(r)
U <- min(U1, n1*n2-U1) # U
V <- n1*n2*(n^3-n-sum(tie^3-tie))/12/(n^2-n) # variance ties considered
E <- n1*n2/2 # Expected
z <- round(((U-E)-ifelse(correct, 0.5, 0))/sqrt(V),3) # z-value
EffectSize.r <- round(abs(z)/sqrt(n),3)
P <- pnorm(abs(z), lower.tail=FALSE)*2
return(structure(list(statistic=c(U=U, "E(U)"=E, "V(U)"=V, "Z-value"=z), p.value=P), class="htest"))
}
pairWiseU <- function(x,y) {
uniqY <- unique(y)
xx <- data.frame(x,y)
yy <- unstack(xx)
for (i in 1:length(uniqY)) {
for (j in 1:length(uniqY)) {
if (i >= j) {
next
} else {
x <- data.frame(yy[i])
y <- data.frame(yy[j])
n1 <- nrow(x)
n2 <- nrow(y)
n <- n1 + n2
resultU <- U.test(x, y, correct=FALSE)
r <- abs(resultU[[1]][[4]])/sqrt(n)
esR.CI <- r.con(r, n, p =.95, twotailed=TRUE)
cat("Comparisons", colnames(x), "-", colnames(y), ":", "\n",
"Mann-Whitney's U:",sprintf("%.3f",round(resultU[[1]][1],4)),"\n",
"z-value:",sprintf("%.3f",round(resultU[[1]][4],4)), "\n",
#p-value (two-tailed without adjustment):",substr(sprintf("%.3f",round(resultU[[2]],4)),2,5), "\n",
"Effect size (r) [95% CI]=", sprintf("%.3f",r), "[", round(esR.CI, 3), "]", "\n",
"\n"
)
}
}
}
}
pairWiseU(dat[,2], dat[,1])
cat("\n")
cat("=============================================================", "\n")
cat("\n", "Pairwise comparisons (p-values)", "\n", "\n")
# cat("\n", "For details see, http://www.med.osaka-u.ac.jp/pub/kid/clinicaljournalclub1.html", "\n")
cat("=============================================================", "\n")
# Bonferroni
cat("\n",
"<< Bonferroni method >>", "\n")
bon <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="bonferroni", exact=F, correct = F)
print(bon)
cat("\n",
"--------------------------------------------------------", "\n")
cat("\n",
"<< Holm-Bonferroni method >>", "\n")
# Holm
holm <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="holm", exact=F, correct = F)
print(holm)
cat("\n",
"--------------------------------------------------------", "\n")
cat("\n",
"<< False Discovery Rate >>", "\n")
# false discovery rate
fdr <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="fdr", exact=F, correct = F)
print(fdr)
cat("\n",
"--------------------------------------------------------", "\n")
})
output$KW.test.out <- renderPrint({
KW.test()
})
# Info
KW.info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
info2 <- paste("It was executed on ", date(), ".", sep = "")
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$KW.info.out <- renderPrint({
KW.info()
})
#-----------------------------------------------------------------
# Friedman test (Differences between several related groups)
#-----------------------------------------------------------------
# For two treatments (k = 2), Friedman test is equivalent to the sign test (not "signed" rank test).
# Wilcoxon signed-rank test is better for k = 2 design.
# Basic statistics
Friedman.bs <- reactive({
dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))
describe(dat)
})
output$Friedman.bs.out <- renderPrint({
Friedman.bs()
})
# Rank
Friedman.ranking <- reactive({
dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))
dtf <- data.frame()
for (i in 1:nrow(dat)){
dtf <- rbind(dtf, rank(dat[i,]))
}
colnames(dtf) <- colnames(dat)
Rank.Mean <- round(apply(dtf[,1:ncol(dtf)], 2, mean),2)
print(Rank.Mean)
})
output$Friedman.ranking.out <- renderPrint({
Friedman.ranking()
})
# Box plot
Friedman.boxPlot <- function(){
dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))
boxplot(dat, las=1)
beeswarm(dat, col = 4, pch = 16, vert = TRUE, add = TRUE)
}
output$Friedman.boxPlot <- renderPlot({
print(Friedman.boxPlot())
})
# Friedman test
Friedman.test <- reactive({
dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))
result <-friedman.test(as.matrix(dat))
print(result)
z <- qnorm(1 - (result$p.value/2)) # p to z
esR <- abs(z)/sqrt(nrow(dat)) # z to r
# 95%CI of r
esR.CI <- r.con(esR, nrow(dat), p =.95, twotailed=TRUE)
cat("Effect size r [95% CI] =", round(esR, 3), "[", round(esR.CI, 3), "]", "\n")
cat(" *Converted from p-value (p -> z -> r)", "\n", "\n", "\n")
#eta2 <- result$statistic[[1]]/((length(dat[,1])*(length(dat[1,])-1)))
#names(eta2) <- NULL
#cat("Effect size (eta-squared) =", sprintf("%.3f",round(eta2,4)), "\n", "\n")
# pair-wise comparisons
cat("=============================================================", "\n")
cat("\n", "Pairwise comparisons (Test statistics and effect sizes)", "\n", "\n")
cat("=============================================================", "\n", "\n")
pairWiseW <- function(x) {
colnames(x) <- c(1:length(x))
a <- as.numeric(colnames(x))
for (i in 1:length(a)) {
for (j in 1:length(a)) {
if (i >= j) {
next
} else {
res <- wilcox.test(x[,i], x[,j], paired=TRUE, correct=FALSE)
pval <- res$p.value
z <- qnorm(1-(pval/2))
r1 <- z/sqrt(length(x[,i])*2)
esR.CI1 <- r.con(r1, length(x[,i]*2), p =.95, twotailed=TRUE)
#r2 <- z/sqrt(length(x[,1])-sum((x[,2]-x[,1])==0))
cat("Comparisons", a[i], "-", a[j], ":", "\n",
"V =", res[[1]][1],"\n",
"z-value =",sprintf("%.3f",round(z,4)), "\n",
#"p-value =",sprintf("%.3f",round(pval,4)), "\n",
"Effect size r [95% CI] =", round(r1, 3), "[", round(esR.CI1, 3), "]", "\n",
#"Effect eize r (without considering ties) =", round(r2, 3), "\n",
"\n")
}
}
}
}
pairWiseW(dat)
cat("\n")
cat("=============================================================", "\n")
cat("\n", "Pairwise comparisons (p-values)", "\n", "\n")
#cat("\n", "For details see, http://www.med.osaka-u.ac.jp/pub/kid/clinicaljournalclub1.html", "\n")
cat("=============================================================", "\n")
x <- stack(dat)
x1 <- x[,1]
x2 <- x[,2]
# Bonferroni
cat("\n",
"<< Bonferroni method >>", "\n")
bon <- pairwise.wilcox.test(x1, x2, p.adj="bonferroni", exact=F, paired=T, correct = F)
print(bon)
cat("\n",
"--------------------------------------------------------", "\n")
cat("\n",
"<< Holm-Bonferroni method >>", "\n")
# Holm
holm <- pairwise.wilcox.test(x1, x2, p.adj="holm", exact=F, paired=T, correct = F)
print(holm)
cat("\n",
"--------------------------------------------------------", "\n")
cat("\n",
"<< False Discovery Rate >>", "\n")
# false discovery rate
fdr <- pairwise.wilcox.test(x1, x2, p.adj="fdr", exact=F, paired=T, correct = F)
print(fdr)
cat("\n",
"--------------------------------------------------------", "\n", "\n")
})
output$Friedman.test.out <- renderPrint({
Friedman.test()
})
# Info
Friedman.info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
info2 <- paste("It was executed on ", date(), ".", sep = "")
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$Friedman.info.out <- renderPrint({
Friedman.info()
})
})
library(shiny)
library(shinyAce)
shinyUI(bootstrapPage(
headerPanel("Non-parametric Tests"),
########## loading message #######################################
tags$head(tags$style(type="text/css", "
#loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 10px 0px 10px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),
###################################################################
mainPanel(
tabsetPanel(position = "left",
# Mann-Whitney U-test (Comparing two independent conditions)
tabPanel("Mann-Whitney U-test",
h2("Mann-Whitney U-test"),
h4("Comparing two independent conditions"),
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 the header (variable names) in the first row.</div></b>")),
aceEditor("text1", value="Class\tScore\n1\t78\n1\t70\n1\t73\n1\t86\n1\t65\n1\t74\n1\t59\n1\t78\n1\t86\n1\t56\n1\t4\n1\t66\n1\t100\n1\t53\n1\t57\n2\t42\n2\t3\n2\t51\n2\t21\n2\t45\n2\t100\n2\t39\n2\t57\n2\t32\n2\t46\n2\t26\n2\t54\n2\t28\n2\t42\n2\t30", mode="r", theme="cobalt"),
br(),
h3("Basic statistics"),
verbatimTextOutput("MWU.bs.out"),
br(),
h3("Ranks"),
verbatimTextOutput("MWU.ranking.out"),
br(),
h3("Box plots with individual data points"),
plotOutput("MWU.boxPlot", width="80%"),
h3("Test result"),
verbatimTextOutput("MWU.test.out"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("MWU.info.out")
),
# Wilcoxon signed-rank test (Comparing two related conditions)
tabPanel("Wilcoxon signed-rank test",
h2("Wilcoxon signed-rank test"),
h4("Comparing two related conditions"),
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 the header (variable names) in the first row.</div></b>")),
aceEditor("text2", value="First\tSecond\n78\t42\n6\t3\n73\t51\n86\t21\n45\t45\n74\t98\n59\t59\n78\t57\n86\t84\n56\t46\n4\t26\n66\t54\n100\t28\n53\t42\n57\t30", mode="r", theme="cobalt"),
br(),
h3("Basic statistics"),
verbatimTextOutput("WSR.bs.out"),
br(),
h3("Ranks"),
verbatimTextOutput("WSR.ranking.out"),
br(),
h3("Box plots with individual data points"),
plotOutput("WSR.boxPlot", width="80%"),
h3("Test result"),
verbatimTextOutput("WSR.test.out"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("WSR.info.out")
),
# Kruskal-Wallis test (Differences between several independent groups)
tabPanel("Kruskal-Wallis test",
h2("Kruskal-Wallis test"),
h4("Differences between several independent groups"),
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 the header (variable names) in the first row.</div></b>")),
aceEditor("text3", value="Class\tScore\n1\t78\n1\t70\n1\t73\n1\t86\n1\t65\n1\t74\n1\t59\n1\t78\n1\t86\n1\t56\n1\t4\n1\t66\n1\t100\n1\t53\n1\t57\n2\t42\n2\t3\n2\t51\n2\t21\n2\t45\n2\t100\n2\t39\n2\t57\n2\t32\n2\t46\n2\t26\n2\t54\n2\t28\n2\t42\n2\t30\n3\t2\n3\t42\n3\t86\n3\t85\n3\t53\n3\t90\n3\t1\n3\t69\n3\t53\n3\t74\n3\t80\n3\t66\n3\t100\n3\t70\n3\t37",mode="r", theme="cobalt"),
br(),
h3("Basic statistics"),
verbatimTextOutput("KW.bs.out"),
br(),
h3("Ranks"),
verbatimTextOutput("KW.ranking.out"),
br(),
h3("Box plots with individual data points"),
plotOutput("KW.boxPlot", width="80%"),
h3("Test result"),
verbatimTextOutput("KW.test.out"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("KW.info.out")
),
# Friedman test (Differences between several related groups)
tabPanel("Friedman test",
h2("Friedman test"),
h4("Differences between several related groups"),
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 the header (variable names) in the first row.</div></b>")),
aceEditor("text4", value="First\tSecond\tThird\n78\t42\t42\n6\t3\t6\n73\t51\t86\n86\t21\t85\n45\t45\t53\n74\t98\t90\n59\t59\t1\n78\t57\t69\n86\t84\t53\n56\t46\t74\n4\t26\t80\n66\t54\t66\n100\t28\t100\n53\t42\t70\n57\t30\t37", mode="r", theme="cobalt"),
br(),
h3("Basic statistics"),
verbatimTextOutput("Friedman.bs.out"),
br(),
h3("Ranks"),
verbatimTextOutput("Friedman.ranking.out"),
br(),
h3("Box plots with individual data points"),
plotOutput("Friedman.boxPlot", width="80%"),
h3("Test result"),
verbatimTextOutput("Friedman.test.out"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("Friedman.info.out")
),
# About
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(psych)'),br(),
code('library(beeswarm)'),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")),
p('The code for this web application is available at',
a('GitHub.', href='https://github.com/mizumot/npt', 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("npt","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/home", 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())
)
)
)
))
