R中有四向交互的有序逻辑回归

时间:2016-08-20 03:37:38

标签: r logistic-regression

我一直尝试使用四向交互执行有序逻辑回归并获取错误消息:

  

设计错误(eval.parent(m)):交互术语不是秒或   第三顺序

设计是: 因变量: 发展阶段 - 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& 2; 饲养环境 - 编号为1和2的2个类别2; 当前环境 - 编号为1和2的2个类别2

由于存在伪复制的可能性,因此模型由" Colony"进行集群运行。

因此,我一直试图使用的代码是:

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?我没有在线搜索备用解决方案的运气,我知道我在尝试使用polr函数进行聚类时遇到了问题。

非常感谢你的帮助。

这是我的数据:

&#13;
&#13;
    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")
&#13;
&#13;
&#13;

1 个答案:

答案 0 :(得分:1)

这只是部分答案。

如果您需要使用群集数据拟合序数模型,则可以使用ordinal包:

流程数据:

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

这里唯一的特殊要点是响应变量需要是有序因子(ordered())。

ordinal中有两个版本的群集累积链接模型(您可以在包的帮助文件中了解他们的选项):

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 - 我认为模型过于复杂,SD计算出现了问题,但需要做更多工作(可能还有更多)才能弄明白。对于两个模型(标准差大约2.7e-5),估计的种群间方差也几乎/实际为零,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:< / p>

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

因为估计的种群间方差为零,所以这给出了与ordinal函数相同的系数估计值。