R中具有四向交互的有序逻辑回归答案

【问题标题】：ordered logistic regression with a four way interaction in RR中具有四向交互的有序逻辑回归
【发布时间】：2016-08-20 03:37:37
【问题描述】：

我一直在尝试使用四向交互执行有序逻辑回归并得到错误消息：

设计错误（eval.parent(m)）：交互项不是第二个或三阶

设计是：因变量：发育阶段 - 5 个级别，编号为 1-5

自变量：年龄 - 收集数据的 4 个不同年龄点；祖先 - 2 个类别，编号为 1 和 2；饲养环境 - 2 个类别，编号为 1 和 2；当前环境 - 2 个类别，编号为 1 和 2

由于存在伪复制的可能性，模型按“殖民地”集群运行。

因此，我一直在尝试使用的代码是：

library(rms)

Data$Ancestry <- factor(Data$Ancestry)
Data$Rearing <- factor(Data$Rearing)
Data$Queenless <- factor(Data$Queenless)

m <- lrm(Level ~ Age *  Ancestry * Rearing * Queenless, x=T, y=T, dat = Data)

robcov(m, cluster = Data$Colony)

我假设错误消息意味着 lrm 不支持 4 向交互。还有另一种方法吗？我没有运气在线搜索替代解决方案，我知道我在尝试使用 polr 函数进行集群时遇到了问题。

非常感谢您的帮助。

这是我的数据：

    structure(list(Bee.Age = c(8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L), Colony = structure(c(1L, 1L, 1L, 1L, 5L, 5L, 
5L, 5L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 9L, 9L, 
9L, 9L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 7L, 
7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 3L, 3L, 3L, 3L, 7L, 
7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 5L, 
5L, 5L, 5L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 9L, 
9L, 9L, 9L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 
7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 3L, 3L, 3L, 3L, 
7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 
5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 
9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 
3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 3L, 3L, 3L, 
3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 
1L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 
1L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 3L, 3L, 
3L, 3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 3L, 3L, 
3L, 3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 1L, 1L, 
1L, 1L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 1L, 1L, 
1L, 1L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 3L, 
3L, 3L, 3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 3L, 
3L, 3L, 3L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L), .Label = c("A1", 
"A2", "A3", "A4", "E1", "E2", "E3", "E4", "I1", "I2"), class = "factor"), 
    Ancestry = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", 
    "2"), class = "factor"), Queenless = structure(c(1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L), .Label = c("1", "2"), class = "factor"), Rearing = structure(c(1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L), .Label = c("1", "2"), class = "factor"), 
    LevelOA = c(1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 
    2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
    1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
    3L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 
    3L, 1L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 
    2L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 3L, 
    1L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 
    3L, 2L, 3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L, 3L, 4L, 3L, 1L, 3L, 
    3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 4L, 2L, 4L, 4L, 2L, 2L, 2L, 
    2L, 3L, 3L, 4L, 4L, 1L, 5L, 1L, 4L, 3L, 1L, 3L, 2L, 4L, 1L, 
    4L, 1L, 3L, 3L, 4L, 3L, 3L, 4L, 4L, 2L, 3L, 3L, 3L, 1L, 3L, 
    2L, 1L, 3L, 3L, 4L, 2L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 5L, 3L, 
    4L, 5L, 1L, 2L, 5L, 3L, 4L, 5L, 5L, 4L, 3L, 1L, 4L, 3L, 4L, 
    2L, 5L, 5L, 4L, 3L, 5L, 4L, 1L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 
    5L, 5L, 2L, 5L, 4L, 4L, 5L, 3L, 5L, 4L, 4L, 5L, 4L, 5L, 2L, 
    4L, 5L, 4L, 5L, 4L, 5L, 4L, 5L), X = c(NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
    )), .Names = c("Bee.Age", "Colony", "Ancestry", "Queenless", 
"Rearing", "LevelOA", "X"), row.names = c(NA, -320L), class = "data.frame")

【问题讨论】：

请提供重现您的错误所需的数据。您还应该始终在代码中包含所有 library 语句。请查看stackoverflow.com/help/mcve 和 R 标签描述：“R 是一种免费的开源编程语言和软件环境，用于统计计算、生物信息学和图形学。请用一个可重复的最小示例补充您的问题。使用@987654327 @ 用于数据并指定所有带有库调用的非基础包。对于统计问题，请使用stats.stackexchange.com。"
您可以坚持使用 polr 并使用集群引导程序获得集群 SE。
根据您在下面发表的评论，您能否澄清您的问题是 1. 如何在运行具有 4 路交互的 logit 回归时解决错误或 2.“我该如何解决此错误和继续使用结果来按菌落对我的数据进行聚类，以消除伪复制的可能性”？因为如果您真的要问后者，那么您的问题既是 Too Broad 又是 Off-topic for StackOverflow。
虽然我很欣赏这项工作，但我们不使用 DropBox 来共享数据。同样，请参阅 R 标签描述和 stackoverflow.com/help/mcve 。请dput 提供最小、完整、可验证示例所需的数据，或使用内置数据集来复制您的错误/问题。在您的代码中创建数据也是可以接受的。我们不使用 Dropbox 等的原因是为了确保数据保持可用和安全。
根据您在下面发表的评论，您能否澄清您的问题是 1. 如何在运行具有 4 路交互的 logit 回归时解决错误或 2.“我该如何解决此错误和继续使用结果来按菌落对我的数据进行聚类，以消除伪复制的可能性”？因为如果您真的要问后者，那么您的问题既是 Too Broad 又是 Off-topic for StackOverflow。如果是前者，那么我相信我已经在下面回答了您的问题。请注意，有 3 位具有 closevote 特权的人投票结束了这个问题。

标签： r logistic-regression

【解决方案1】：

这只是部分答案。

如果您需要用聚类数据拟合有序模型，可以使用ordinal 包：

过程数据：

 library(ordinal)
 Data <- transform(Data,
     Ancestry=factor(Ancestry),
     Rearing=factor(Rearing),
     Queenless=factor(Queenless),
     LevelOA=ordered(LevelOA))

这里唯一特别的一点是响应变量需要是有序因子（ordered()）。

ordinal 中有两个版本的集群 cumulative-link 模型（您可以在包的帮助文件中了解它们的选项）：

c1 <- clmm(LevelOA ~ Bee.Age*Ancestry*Rearing*Queenless + (1|Colony),
     dat = Data)

c2 <- clmm2(ordered(LevelOA) ~ Bee.Age*Ancestry*Rearing*Queenless,
      random = Colony, data= Data, Hess=TRUE)

这两个都给出了估计值，但没有给出标准偏差 - 我认为模型太复杂了，SD 计算出了点问题，但要弄清楚它需要更多的工作（也许更多）。估计的群体间方差对于两个模型也几乎/实际上为零（std.dev 约 2.7e-5），这意味着作为初步，您可以使用 MASS::polr 来拟合模型而不进行聚类：

c0 <- polr(LevelOA ~ Bee.Age *  Ancestry * Rearing * Queenless,
     dat = Data)

因为估计的群体间方差为零，这给出了与ordinal 函数相同的系数估计值。

【讨论】：

谢谢本。我期待着在早上尝试您的解决方案。