例：8例肝硬化病人部分肝切除前後蛋白變化（PAT.mg/L）
subj <- gl(8,1,48);
time <-gl(6,8,48,labels=
c("day0","day2","day5","day10","day15","day20"));
y <-c(177,245,111,203,102,280,330,320,117,172,100,181,80,260,280,100,
140,125,150,94,79,145,210,187,159,86,95,121,87,230,210,250,220,180,108,192,162,350,380,190, 105,245,128,220,143,240,240,290)
repdata <- data.frame(subj,time,y);repdata
subj time y
1 1 day0 177
2 2 day0 245
3 3 day0 111
4 4 day0 203
5 5 day0 102
6 6 day0 280
7 7 day0 330
8 8 day0 320
9 1 day2 117
10 2 day2 172
11 3 day2 100
12 4 day2 181
13 5 day2 80
14 6 day2 260
15 7 day2 280
16 8 day2 100
17 1 day5 140
18 2 day5 125
19 3 day5 150
20 4 day5 94
21 5 day5 79
22 6 day5 145
23 7 day5 210
24 8 day5 187
25 1 day10 159
26 2 day10 86
27 3 day10 95
28 4 day10 121
29 5 day10 87
30 6 day10 230
31 7 day10 210
32 8 day10 250
33 1 day15 220
34 2 day15 180
35 3 day15 108
36 4 day15 192
37 5 day15 162
38 6 day15 350
39 7 day15 380
40 8 day15 190
41 1 day20 105
42 2 day20 245
43 3 day20 128
44 4 day20 220
45 5 day20 143
46 6 day20 240
47 7 day20 240
48 8 day20 290
install.packages("gplots")
library(gplots)
plotmeans(y~time)
方法1：
fit<- aov(y~time+subj)
summary(fit)
install.packages("car")
library(car)
Anova(fit,type="3")
interaction.plot(time,subj,y)
TukeyHSD(fit,"time")
方法2：
install.packages("ez")
library(ez)
model <- ezANOVA(repdata,y,subj,within=time,detailed=T,
return_aov = T,type=3)
model
方法3：
install.packages("nlme")
library(nlme)
am2 <- lme(y~time,random=~1|subj/time,data=repdata)
summary(am2)
anova(am2)
install.packages("multcomp")
library(multcomp)
summary(glht(am2,linfct=mcp(time="Tukey")))
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