#################  
### ANOVA one way
#################

### Example 3-1 page 
d<-file.choose()
d
data13<-read.csv(d,header=TRUE)
data13

data13$treat
data13$yij


boxplot(yij~treat,data=data13,col="gray80",names=c("15%","20%","25%","30%","35%"))

plot(data13$treat,data13$yij)

summary(data13$yij)

mean(data13$yij)

?aggregate
aggregate(yij~treat,data=data13,FUN=mean)
aggregate(yij~treat,data=data13,FUN=sum)
aggregate(yij~treat,data=data13,FUN=summary)

###################
yij<-data13$yij
treat<-data13$treat

yij
treat

ystar<-yij-10
ystar

##################
?aov

## factor
is.factor(data13$treat)

data13$treat<-as.factor(data13$treat)
is.factor(data13$treat)
data13$treat

M<-aov(yij~treat,data=data13)

M
anova(M)

model.tables(M)
#########################################
### paired comparison ### 
#########################################

## Tukey Honest Significant Differences

TukeyHSD(M)


library(agricolae)

?LSD.test
out<-LSD.test(M,"treat")
out

plot(out)


out1<-duncan.test(M,"treat")
out1

######## check model

R<-resid(M)
R

boxplot(R)

Fit<-fitted(M)
Fit
## normality Residual
qqnorm(R)
qqline(R)

shapiro.test(R)

## equal of variance residuals

?var.test

plot(Fit,R)

bartlett.test(R~treat) ## parametric test
fligner.test(R~treat)     ## nonparametric test

## autocorrelation
ts.plot(R)
points(R,col="red",pch=16)
abline(h=0,lty=2)

##Durbin-Watson Test
library(lmtest)
?dwtest

dwtest(M)


##############################
 ### block Design  ####
##############################

#########################
## example 1-5 page 162
#########################

data15<-read.csv("data1-5.csv",header=T)
data15

data15$treat<-as.factor(data15$treat)
data15$block<-as.factor(data15$block)

## describtive statistics



boxplot(yij~treat,data=data15,col="gray80")
boxplot(yij~block,data=data15,col="gray80")

plot(data15$treat,data15$yij)

summary(data15$yij)

mean(data15$yij)

?aggregate
aggregate(yij~treat,data=data15,FUN=mean)
aggregate(yij~block,data=data15,FUN=mean)
aggregate(yij~treat,data=data15,FUN=sum)

aggregate(yij~treat,data=data15,FUN=summary)


### ANOVA 
M<-aov(yij~treat+block,data=data15)
M

anova(M)

######## check model

R<-resid(M)
R

boxplot(R)

Fit<-fitted(M)
Fit
## normality Residual
qqnorm(R)
qqline(R)

shapiro.test(R)

## equal of variance residuals

?var.test

plot(Fit,R)

bartlett.test(R~data15$treat) ## parametric test
fligner.test(R~data15$treat)     ## nonparametric test

## autocorrelation
ts.plot(R)
points(R,col="red",pch=16)
abline(h=0,lty=2)

##Durbin-Watson Test
library(lmtest)
?dwtest

dwtest(M)



##############################
 ### Latin Sqaure Design  ####
##############################

#########################
## example 2-5 page 182
#########################

data25<-read.table("Data2-5.txt",header=T)

#write.csv(x,file="Data2-5.csv")


data25$Treat<-as.factor(data25$Treat)
data25$Row<-as.factor(data25$Row)
data25$Col<-as.factor(data25$Col)

## describtive statistics



boxplot(Yij~Treat,data=data25,col="gray80")
boxplot(Yij~Row,data=data25,col="gray80")


summary(data25$Yij)

mean(data25$Yij)

?aggregate
aggregate(Yij~Treat,data=data25,FUN=mean)
aggregate(Yij~Treat,data=data25,FUN=sum)

aggregate(Yij~Row,data=data25,FUN=mean)
aggregate(Yij~Col,data=data25,FUN=mean)

aggregate(Yij~Treat,data=data25,FUN=summary)
#############


### ANOVA 
M<-aov(Yij~Treat+Row+Col,data=data25)
M

anova(M)

#########################################
### paired comparison ### 
#########################################

## Tukey Honest Significant Differences

TukeyHSD(M)


library(agricolae)

?LSD.test
out<-LSD.test(M,"Treat")
out

plot(out)


out1<-duncan.test(M,"Treat")
out1

######## check model

R<-resid(M)
R

boxplot(R)

Fit<-fitted(M)
Fit
## normality Residual
qqnorm(R)
qqline(R)

shapiro.test(R)

## equal of variance residuals

plot(Fit,R)

bartlett.test(R~data25$Treat) ## parametric test

fligner.test(R~data25$Treat)     ## nonparametric test

## autocorrelation
ts.plot(R)
points(R,col="red",pch=16)
abline(h=0,lty=2)

##Durbin-Watson Test
library(lmtest)

dwtest(M)

##########################