###########################################
#Kline, R. B. (2004). 
#Parametric effect size indexes. 
#In Beyond significance testing: Reforming data analysis #methods in behavioral research (pp. 93-142). 
#Washington, DC: American Psychological Association.
#
#データ元:Beyond significance testing resource guide
#http://www.apa.org/books/resources/kline/
###########################################

#---------------------------------------------------#
#uncompleted job
#---------------------------------------------------#
#RTRの値が教科書と一致しない
#t値が教科書と一致しない
#Mdnが教科書では意味不明
#したがって，Cohen のU3も計算せず．
#Iは，算出できず
#---------------------------------------------------#

source("http://blue.zero.jp/yokumura/R/kline/paraes.r")
#--------------------------------#
#Step1: データハンドリング
#--------------------------------#
data1 <- read.csv("http://blue.zero.jp/yokumura/R/kline/chap4.csv",header=T)
head(data1)
tail(data1)
names(data1)
data1$GENDER <- factor(data1$GENDER, label=c("men","women"))



#--------------------------------#
#Step2: 基礎統計量
#--------------------------------#
attach(data1)

##(a)年齢
mean(AGE, na.rm=T)
sd(AGE, na.rm=T)


##(b) 人数
table(GENDER)
prop.table(table(GENDER))


##(c) 正答数
tapply(MATHTOT, GENDER, mean)
tapply(MATHTOT/17*100, GENDER, mean)
tapply(MATHTOT, GENDER, var)
tapply(MATHTOT, GENDER, median)


#(d) 変数の作成
x1 <- MATHTOT[GENDER=="men"]
x2 <- MATHTOT[GENDER=="women"]

detach(data1)



#--------------------------------#
#Step3: 効果量
#--------------------------------#

##(a) samp.stat(x1, x2)
#--------------------------------#
#両群の，
#平均，（不偏）標準偏差，
#標本サイズ，平均値差を算出
#--------------------------------#
samp.stat(x1,x2)


##(b) ind2group(x1,x2,sig.level=.05)
#--------------------------------#
#Cohen's d   = g
#Glass's ES  = glass1, glass2
#Hedges's g  = delta
#delta.lower = Exact Confidence Interval
#delta.upper = 
#t.value     = t.value (equal variance)
#df	     = degree of freedom
#p.value
#rpb         = point-biserial correlation
#eta.sq	     = eta square
#eta.sq.lower= Exact eta square
#eta.sq.upper=
#--------------------------------#
ind2group(x1,x2)


##(c) ind2case(x1,x2,sig.level=.05,Cut=1)
#--------------------------------#
#CL = common language effect size
#RTR = right-tail ratio (default: Cut=1)
#cut.sd = mean + sd * Cut
#freq1  = frequancy distribution of x1
#freq2  = frequancy distribution of x2
#--------------------------------#
ind2case(x1,x2)


boxplot(x1,x2)
graphics.off()