################# ### 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) ##########################