如何进行迭代方差分析并从R中的lm对象中提取均方值

我有一个数据集，其中有18个群体。每个群体中都有几个个体，每个个体都有一个“颜色”的称呼。我只想在单因素方差分析中同时比较两个群体，以群体为主要因素，以获得内部和之间的成对MS

我知道如何使用以下代码从综合方差分析中提取MS：

mylm <- lm(Color ~ Pop, data=PopColor)
anova(mylm)[["Mean Sq"]]

有没有一种方法可以让我创建一个do循环，在所有人群之间进行所有成对的单向方差分析，然后在MS之间和MS内提取出数据

然后，我想将两个MS值从每次比较移动到它们自己的对称矩阵：一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者之间，一个在受试者。这些列和行的名称与总体名称相同

下面是我的数据的一个子集，共有六个群体：

dput(dat)
structure(list(Pop = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("pop1001", "pop1026", 
"pop252", "pop254a", "pop311", "pop317"), class = "factor"), 
    Color = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 
    3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 
    3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 
    3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 
    2L, 3L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 4L, 
    4L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 
    2L, 3L, 4L, 2L, 2L, 4L, 3L)), .Names = c("Pop", "Color"), class = "data.frame", row.names = c(NA, 
-94L))

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任何帮助都将不胜感激！谢谢

我不明白你的第2点（对于不像我这样的统计学家来说有点技术性）。对于第一点，我理解为您希望在所有对人群中应用lm/Anova。您可以使用

combn

：

combn：一次生成x和m元素的所有组合

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pops欢迎来到SO。我对你的问题提出了一点看法。请阅读SO关于如何设置问题格式的帮助。格式良好的问题通常会得到最好的答案。感谢您重新格式化我的问题！您最好使用
multcomp
软件包，该软件包还可以更正多次比较。@hadley是的，我将向general/omnibus ANOVA模型添加bonferroni更正。现在我只需要MS，多次比较修正只会改变F重要性的临界值，而不会改变实际MS值。但是谢谢-我会调查multcomp软件包。我需要15点声誉积分，所以我无法撤销否决票！谢谢你的回答，这真的很有帮助！不客气。我不是专家，但就个人而言，我会尝试@hadley建议使用
multicmp
软件包
dput(dat)
structure(list(Pop = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("pop1001", "pop1026", 
"pop252", "pop254a", "pop311", "pop317"), class = "factor"), 
    Color = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 
    3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 
    3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 
    3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 
    2L, 3L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 4L, 
    4L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 
    2L, 3L, 4L, 2L, 2L, 4L, 3L)), .Names = c("Pop", "Color"), class = "data.frame", row.names = c(NA, 
-94L))

pops <- unique(PopColor$Pop)
ll <- combn(pops,2,function(x){
                  dat.pair <- PopColor[PopColor$Pop %in% pops[x],]
                  mylm <- lm(Color ~ Pop, data=dat.pair)
                  c(as.character(pops[x]),anova(mylm)[["Mean Sq"]])
},simplify=FALSE)
do.call(rbind,ll)
     [,1]      [,2]      [,3]                 [,4]               
 [1,] "pop1026" "pop254a" "0.210291858678956"  "0.597865353037767"
 [2,] "pop1026" "pop1001" "0.52409988385598"   "0.486874236874237"
 [3,] "pop1026" "pop317"  "15.7296466973886"   "0.456486042692939"
 [4,] "pop1026" "pop311"  "1.34392057218144"   "0.631962930099576"
 [5,] "pop1026" "pop252"  "0.339324116743472"  "0.528899835796388"
 [6,] "pop254a" "pop1001" "0.0166666666666669" "0.351785714285714"
 [7,] "pop254a" "pop317"  "14.45"              "0.227777777777778"
 [8,] "pop254a" "pop311"  "1.92898550724637"   "0.561430575035063"
 [9,] "pop254a" "pop252"  "0.8"                "0.344444444444444"
[10,] "pop1001" "pop317"  "20.4166666666667"   "0.205357142857143"
[11,] "pop1001" "pop311"  "3.55030333670374"   "0.46474019088017" 
[12,] "pop1001" "pop252"  "1.35"               "0.280357142857143"
[13,] "pop317"  "pop311"  "9.60474308300398"   "0.429172510518934"
[14,] "pop317"  "pop252"  "8.45"               "0.116666666666667"
[15,] "pop311"  "pop252"  "0.110803689064557"  "0.496914446002805"