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我正在尝试研究如何进行重复测量方差分析。我的数据结构如下

    means <- structure(list(col = c("c", "v1", "b1", "v2", "b2"), 
                `1` = c(8.55,9.73, 8.93, 9.52, 9.91), 
                `2` = c(8.4, 9.97, 9.08, 9.66, 9.97),
                `3` = c(8.48, 10.04, 9.13, 9.73, 10.04),
                `4` = c(8.42, 9.63,8.9, 9.34, 9.82), 
                `5` = c(8.42, 9.59, 8.87, 9.39, 9.69),
                `6` = c(8.52, 9.74, 9.02, 9.58, 9.84), 
                `7` = c(8.37, 9.67,8.98, 9.47, 9.74), 
                `8` = c(8.42, 9.67, 9.02, 9.52, 9.77), 
                `9` = c(8.56, 9.79, 9.36, 9.6, 9.78), 
                `10` = c(8.44, 9.63,9.15, 9.52, 9.67), 
                `11` = c(8.3, 9.58, 9.05, 9.49, 9.63),
                `12` = c(8.03, 9.33, 8.82, 9.23, 9.38),
                `13` = c(7.95, 9.08, 8.7, 9.04, 9.19), 
                `14` = c(8, 8.34, 8.37, 8.43, 8.54), 
                `15` = c(8.04,8.26, 8.4, 8.45, 8.61), 
                `16` = c(8.08, 8.09, 8.18, 8.16,8.28), 
                `17` = c(7.99, 8.06, 8.09, 8.15, 8.26), 
                `18` = c(8.06, 8.06, 8.09, 8.1, 8.22), 
                `19` = c(7.96, 7.96, 7.99, 8.03, 8.1),
                `20` = c(7.96, 7.98, 7.99, 7.99, 8.11), 
                `21` = c(8.16, 8.22, 8.22, 8.26, 8.33), 
                `22` = c(8.08, 8.16, 8.13, 8.2, 8.2), 
                `23` = c(7.94, 7.97, 7.94, 7.98, 8.07), 
                `24` = c(8.02,8.03, 8, 8.08, 8.1), 
                `25` = c(8.03, 8.08, 8.09, 8.12, 8.15), 
                `26` = c(7.92, 7.95, 7.95, 7.96, 7.98)), 
                        .Names = c("col","1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12",
                                 "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23","24", "25", "26"), 
                                        class = c("data.table", "data.frame"))

其中“col”代表不同的基材（处理），标题中的数字是随时间变化的测量值。这只是部分数据。

为了进行重复测量方差分析（希望是正确的统计测试），我尝试遵循我在网上找到的几个示例，例如http://rtutorialseries.blogspot.de/2011/02/r-tutorial-series-one -way-repeated.html

    # step 1 Define the levels:
    levels <- c(1:26)
    # define factor
    factor <- as.factor(levels)
    #define the frame
    frame <- data.frame(factor)
    # bind the colums
    bind <- cbind (means$`1`,means$`2`,means$`3`,means$`4`,means$`5`,means$`6`,means$`7`,means$`8`,means$`9`,means$`10`,means$`11`,means$`12`,means$`13`,means$`14`,means$`15`,means$`16`,means$`17`,means$`18`,means$`19`,means$`20`,means$`21`,means$`22`,means$`23`,means$`24`,means$`25`,means$`26`)


# define the model
model <- lm(ph_bind ~ 1)
# ANOVA
analysis <- Anova(model, idata=frame, idesign= ~factor)

这导致：

> analysis <- Anova(model, idata = factor, idesign = ~factor)
Warning message:
In Anova.lm(model, idata = factor, idesign = ~factor) :
  the model contains only an intercept: Type III test substituted
> summary (analysis)
     Sum Sq              Df         F value          Pr(>F) 
 Min.   :  61.59   Min.   :  1   Min.   :20519   Min.   :0  
 1st Qu.:2495.43   1st Qu.: 33   1st Qu.:20519   1st Qu.:0  
 Median :4929.27   Median : 65   Median :20519   Median :0  
 Mean   :4929.27   Mean   : 65   Mean   :20519   Mean   :0  
 3rd Qu.:7363.10   3rd Qu.: 97   3rd Qu.:20519   3rd Qu.:0  
 Max.   :9796.94   Max.   :129   Max.   :20519   Max.   :0  
                                 NA's   :1       NA's   :1

这不是我希望的预期输出。我究竟做错了什么？

感谢您的帮助:)

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