Monthly Archives: July 2017

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データ解析基礎論A week12

dat<-read.csv("http://peach.l.chiba-u.ac.jp/course_folder/datW03R.txt")

means<-tapply(dat$shoesize,list(dat$gender, dat$affil),mean)
Ns<-tapply(dat$shoesize,list(dat$gender, dat$affil),length)
sds<-tapply(dat$shoesize,list(dat$gender, dat$affil),sd)
sems<-sds/sqrt(Ns)
plot(c(0,1),means[,1],type='o',col='skyblue', xlim=c(-0.1,1.1), lwd=2, 
     cex=2, pch=20, ylim=c(min(means)*0.975, max(means)*1.025), xlab="gender", ylab="shoesize",
     xaxt="n")
axis(1,c(0,1),c("female","male"))
lines(c(0,1),means[,2],type='o',col='orange',lwd=2,cex=2,pch=20)
lines(c(0,0),c(means[1,1]-sems[1,1],means[1,1]+sems[1,1]),col="skyblue",lwd=2)
lines(c(0,0),c(means[1,2]-sems[1,2],means[1,2]+sems[1,2]),col="orange",lwd=2)
lines(c(1,1),c(means[2,1]-sems[2,1],means[2,1]+sems[2,1]),col="skyblue",lwd=2)
lines(c(1,1),c(means[2,2]-sems[2,2],means[2,2]+sems[2,2]),col="orange",lwd=2)
legend("topleft",c("CogSci","PsySci"),col=c("skyblue","orange"),lwd=2)


interaction.plot(dat$gender,
                 dat$affil,
                 dat$shoesize, 
                 pch=c(20,20), 
                 col=c("skyblue","orange"), 
                 xlab="gender", ylab="shoesize", 
                 lwd=3,type='b',cex=2,
                 trace.label="Affiliation")

dat.aov=aov(shoesize~gender*affil, data=dat)
dat.aov.sum=summary(dat.aov)

# testing simple main effect of gender
means<-tapply(dat$shoesize, list(dat$gender,dat$affil), mean)
SS_gen_CS<- 5*(means[2,1]^2 + means[1,1]^2 -0.5*sum(means[,1])^2) # SS_gender CS
SS_gen_PS<- 5*(means[2,2]^2 + means[1,2]^2 -0.5*sum(means[,2])^2) # SS_gender PS
dat.aov.sum=summary(dat.aov)   # ANOVA table
MSe=dat.aov.sum[[1]][4,3]      # MSE from ANOVA table or MSe=0.62
dfE=dat.aov.sum[[1]][4,1]      # DF for error or dfE=16
dfG=1                          # DF for gender
F_gen_CS=(SS_gen_CS/dfG)/MSe   # F-value for gender effect given CS
F_gen_PS=(SS_gen_PS/dfG)/MSe   # F-value for gender effect given PS
P_gen_CS=1-pf(F_gen_CS,1,dfE)  # p-value for gender effect given CS
P_gen_PS=1-pf(F_gen_PS,1,dfE)  # p-value for gender effect given PS

# testing simple main effect of affiliation
SS_affil_F<- 5*(means[1,1]^2+means[1,2]^2-0.5*sum(means[1,])^2) #SS_affil | F
SS_affil_M<- 5*(means[2,1]^2+means[2,2]^2-0.5*sum(means[2,])^2) #SS_affil | M
dfA=1				          # DF for affil
F_affil_F=SS_affil_F/dfA/MSe         # F-value for affiliation effect | F
F_affil_M=SS_affil_M/dfA/MSe         # F-value for affiliation effect | M
P_affil_F=1-pf(F_affil_F,1,dfE)      # p-value for affiliation effect | F
P_affil_M=1-pf(F_affil_M,1,dfE)      # p-value for affiliation effect | M

# ANOVA 2x4 
dat<-read.csv("http://www.matsuka.info/data_folder/dktb321.txt")
interaction.plot(dat$duration,dat$method,dat$result, pch=c(20,20), col=c("skyblue","orange"), ylab="score", xlab="Duration",lwd=3,type='b',cex=2,trace.label="Method")
dat.aov=aov(result~method*duration,data=dat)

means<-tapply(dat$result,list(dat$method,dat$duration),mean)
ssM_1=5*(sum(means[,1]^2)-0.5*(sum(means[,1])^2))
ssM_2=5*(sum(means[,2]^2)-0.5*(sum(means[,2])^2))
ssM_3=5*(sum(means[,3]^2)-0.5*(sum(means[,3])^2))
ssM_4=5*(sum(means[,4]^2)-0.5*(sum(means[,4])^2))
MSe=mod2.sum[[1]][4,3]
DFe=mod2.sum[[1]][4,1]
DFm=1
fM_1=(ssM_1/DFm)/MSe
1-pf(fM_1,DFm,DFe)

ssD_X=5*(sum(means[1,]^2)-1/4*(sum(means[1,])^2))
ssD_Y=5*(sum(means[2,]^2)-1/4*(sum(means[2,])^2))
DFd=3
fD_X=(ssD_X/DFd)/MSe
fD_Y=(ssD_Y/DFd)/MSe
1-pf(fD_X,DFd,DFe)
1-pf(fD_X,DFd,DFe)

qv=qtukey(0.95,DFd+1,DFe)
hsd=qv*(sqrt(MSe/5))
print(diffM<-outer(means[1,],means[1,],"-"))           
abs(diffM)>hsd    

# useful r functions
source("http://peach.l.chiba-u.ac.jp/course_folder/tsme2017.R")
CRF.tsme(dat.aov, dat)
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データ解析基礎論A week11

f=c(24.1,23.9,24.4,24.4,23.5)
m=c(25.6,26.1,25.8,25.9,26)

G.mean=mean(c(f,m))
ss.total=sum((c(f,m)-G.mean)^2)
ss.tr=sum((mean(f)-G.mean)^2)*5+sum((mean(m)-G.mean)^2)*5
ss.error=sum((f-mean(f))^2)+sum((m-mean(m))^2)

dat<-data.frame(ssize=c(f,m),gender=c(rep("f",5),rep("m",5)))
dat.aov<-aov(ssize ~ gender,data=dat)
dat<-read.table("http://www.matsuka.info/data_folder/aov01.txt")
dat.aov<-aov(shoesize~club, dat)

qT<-qtukey(0.95, 3, 67)
HSD<-qT*sqrt((2.243*(1/23+1/24))/2)


dat<-read.csv("http://www.matsuka.info/data_folder/dktb312.txt",
              col.names=c("dump","method","result"))
levels(dat$method)<-c('free','repeat','sentence','image')
dat.aov<-aov(result~method, data=dat)
summary(dat.aov)


dat.means<-tapply(dat$result,dat$method,mean)
cont=c(-3,1,1,1)
bunshi=sum(cont*dat.means)
bunbo=sqrt(5.29*(sum((cont^2)/8)))

t.value=bunshi/bunbo
2*(1-pt(t.value,28))

dat.means<-tapply(dat$result,dat$method,mean)
cont=c(-3,1,1,1)
bunshi=sum(cont*dat.means)
bunbo=sqrt(5.29*(sum((cont^2)/8)))
F.value=(bunshi/bunbo)^2
new.F=3*qf(0.95,3,28)