生物统计10.pptVIP

生物统计学 2009年度 计算生物所 方差分析（下） 5月22日 How to assign vectors controlA=c(0.22, -0.87, -2.39, -1.79, 0.37, -1.54, 1.28, -0.31, -0.74, 1.72, 0.38, -0.17, -0.62, -1.10, 0.30, 0.15, 2.30, 0.19, -0.50, -0.09) treatmentA=c(-5.13, -2.19, -2.43, -3.83, 0.50, -3.25, 4.32, 1.63, 5.18, -0.43, 7.11, 4.87, -3.10, -5.81, 3.76, 6.31, 2.58, 0.07, 5.76, 3.50) Now, controlA and treatmentA are two numeric data vectors. x=1:10 x=seq(1,10,by=0.5) x=rep(1:10,2) x=rep(1:10,each=2) x=as.character(c(ABC,DEF)) x=as.factor(c(rep(treatment,5),rep(control,5))) How to read and write in R To read in a data file: iris=read.table(~/Desktop/bio-statistics/iris_data.txt,header=T,sep=\t) ~/Desktop/bio-statistics/iris_data.txt” is the file name under the directory: ~/Desktop/bio-statistics/ header=T means the file has a header sep=“\t” means the data separator is the tab “\t” To write data to a file: write.table(iris, ~/Desktop/bio-statistics/iris_data_test.txt,col.names=T,row.names=F,quote=F,sep=\t) Iris data is written to file ~/Desktop/bio-statistics/iris_data_test.txt” while keeping the header without the row.names, without the quotation marks, and with tab “\t” as the separator. 多因子方差分析 多因子方差分析 多因子方差分析 系统分组随机区组试验 多因子方差分析 多因子方差分析 多因子方差分析 多因子方差分析 多因子方差分析 多因子方差分析 作业 课本208-209页，4，6，7。 * 交叉分组随机区组试验 例7.3 a=c(8,7,6,9,7,8,7,8,10) b=c(8,7,5,9,9,7,7,7,9) c=c(8,6,6,8,6,6,6,8,9) x=c(a, b, c) A=as.factor(rep(rep(1:3,each=3),3)) B=as.factor(rep(rep(1:3,3),3)) C=as.factor(rep(1:3,each=9)) #TR=as.factor(rep(1:9,3)) summary(aov(x~A*B+C)) 例7.3 summary(aov(x~A*B+C)) Df Sum Sq Mean Sq F value Pr(F) A 2 6.2222 3.1111 6.4000 0.0090744 ** B 2 1.5556 0.7778 1.6000 0.2325680 C 2 2.8889 1.4444 2.9714 0.0799125 . A:B 4 22.2222 5.5556 11.4286 0.0001414 *** Residuals 16 7.7778 0.4861 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 多因子方差分析 例7.4 x11=c(2.5, 3.5, 3.0) x12=c(5.0, 4.5, 3.5) x13=c(5.5, 5.5, 5.0)