“特质”的含义在MCMCglmm中 [英] Meaning of "trait" in MCMCglmm

问题描述

就像这篇文章我正在为 MCMCglmm 的符号而苦苦挣扎，尤其是 trait 。我的代码如下

Like in this post I'm struggling with the notation of MCMCglmm, especially what is meant by trait. My code ist the following

library("MCMCglmm")

set.seed(123)
y <- sample(letters[1:3], size = 100, replace = TRUE)
x <- rnorm(100)
id <- rep(1:10, each = 10)
dat <- data.frame(y, x, id)

mod <- MCMCglmm(fixed = y ~ x, random = ~us(x):id,
            data = dat,
            family = "categorical")

这给了我错误消息对于涉及超过2类类别的分类数据的错误结构，请使用trait：units或variance.function（trait）：units。（！sic）。如果我通过 letters [1：2] 生成二分数据，那么一切都会很好。那么，此错误消息的一般含义是什么，尤其是特征是什么意思？

Which gives me the error message For error structures involving catgeorical data with more than 2 categories pleasue use trait:units or variance.function(trait):units. (!sic). If I would generate dichotomous data by letters[1:2], everything would work fine. So what is meant by this error message in general and "trait" in particular?

编辑2016-09 -29：
来自链接的问题我将 rcov =〜us（trait）：units 复制到了 MCMCglmm 。并且来自 https://stat.ethz.ch/ pipermail / r-sig-mixed-models / 2010q3 / 004006.html 我取了（并稍加修改了）先前的
list（R = list（V = diag（2 ），fix = 1），G = list（G1 = list（V = diag（2），nu = 1，alpha.mu = c（0，0），alpha.V = diag（2）* 100）））） 。现在我的模型实际上给出了结果：

Edit 2016-09-29: From the linked question I copied rcov = ~ us(trait):units into my call of MCMCglmm. And from https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004006.html I took (and slightly modified it) the prior list(R = list(V = diag(2), fix = 1), G = list(G1 = list(V = diag(2), nu = 1, alpha.mu = c(0, 0), alpha.V = diag(2) * 100))). Now my model actually gives results:

MCMCglmm(fixed = y ~ 1 + x, random = ~us(1 + x):id, 
         rcov = ~ us(trait):units, prior = prior, data = dat,
         family = "categorical")

但是我仍然缺乏对特征的含义（以及<$ c $的含义）的理解c>单位和先前的表示法， us（）与 idh（）和...）。

But still I've got a lack of understanding what is meant by trait (and what by units and the notation of the prior, and what is us() compared to idh() and ...).

编辑2016-11-17：
我认为 trait 通常等同于目标变量或响应，在本例中为 y 。在随机的公式中，〜的左侧没有任何内容>因为从固定效果规范中知道了响应。 后面说明 rcov 需要 trait：units 可能是因为它已经由固定公式，特征是什么（在这种情况下为 y ）。

Edit 2016-11-17: I think trait is synoym to "target variable" or "response" in general or y in this case. In the formula for random there is nothing on the left side of ~ "because the response is known from the fixed effect specification." So the rational behind specifiying that rcov needs trait:units could be that it is alread defined by the fixed formula, what trait is (y in this case).

推荐答案

单位是响应变量值和特征是响应变量名称，与类别相对应。通过指定 rcov =〜us（trait）：units ，您可以使剩余方差在特征（响应类别）之间是异构的，以便所有剩余方差的元素-协方差矩阵将被估计。

units is the response variable value, and trait is the response variable name, which corresponds to the categories. By specifying rcov = ~us(trait):units, you are allowing the residual variance to be heterogeneous across "traits" (response categories) so that all elements of the residual variance-covariance matrix will be estimated.

在哈德菲尔德（Hadfield）MCMCglmm课程笔记（ vignette（ CourseNotes， MCMCglmm））的5.1节中，您可以阅读有关保留变量 trait 和 units 的说明。

In Section 5.1 of Hadfield's MCMCglmm Course Notes (vignette("CourseNotes", "MCMCglmm")) you can read an explanation for the reserved variables trait and units.

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