R中的MCMCglmm多项式模型 [英] MCMCglmm multinomial model in R

问题描述

我正在尝试使用R中的MCMCglmm包创建模型.

I'm trying to create a model using the MCMCglmm package in R.

数据的结构如下，其中dyad，focus，其他均为随机效应，predest1-2为预测变量，而Response 1-5为结果变量，捕获了#个不同亚型的观察到的行为:

The data are structured as follows, where dyad, focal, other are all random effects, predict1-2 are predictor variables, and response 1-5 are outcome variables that capture # of observed behaviors of different subtypes:

 dyad focal other r    present  village  resp1 resp2 resp3 resp4 resp5 
 1    10101 14302 0.5  3        1        0     0     4     0     5
 2    10405 11301 0.0  5        0        0     0     1     0     1
 …

所以只有一个结果(教学)的模型如下:

So a model with only one outcome (teaching) is as follows:

 prior_overdisp_i <- list(R=list(V=diag(2),nu=0.08,fix=2), 
 G=list(G1=list(V=1,nu=0.08), G2=list(V=1,nu=0.08), G3=list(V=1,nu=0.08), G4=list(V=1,nu=0.08)))

 m1 <- MCMCglmm(teaching ~ trait-1 + at.level(trait,1):r + at.level(trait,1):present, 
 random= ~idh(at.level(trait,1)):focal + idh(at.level(trait,1)):other + 
 idh(at.level(trait,1)):X + idh(at.level(trait,1)):village, 
 rcov=~idh(trait):units, family = "zipoisson", prior=prior_overdisp_i, 
 data = data, nitt = nitt.1, thin = 50, burnin = 15000, pr = TRUE, pl = TRUE, verbose = TRUE, DIC  = TRUE)

Hadfield的课程注释(第5章)给出了一个多项式模型的示例，该模型仅使用具有3个级别(3种类型的羊角)的单个结果变量.可以在此处找到类似的处理方法: http://hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/对于我正在做的事情，这不太正确，但其中包含有用的背景信息.

Hadfield's course notes (Ch 5) give an example of a multinomial model that uses only a single outcome variable with 3 levels (sheep horns of 3 types). Similar treatment can be found here: http://hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/ This is not quite right for what I'm doing, but contains helpful background info.

另一个参考文献(Hadfield，2010年)给出了多响应MCMCglmm的示例，该示例遵循相同的格式，但使用cbind()预测响应的向量，而不是单个结果.具有多个响应的相同模型如下所示:

Another reference (Hadfield 2010) gives an example of a multi-response MCMCglmm that follows the same format but uses cbind() to predict a vector of responses, rather than a single outcome. The same model with multiple responses would look like this:

 m1 <- MCMCglmm(cbind(resp1, resp2, resp3, resp4, resp5) ~ trait-1 + 
 at.level(trait,1):r + at.level(trait,1):present, 
 random= ~idh(at.level(trait,1)):focal + idh(at.level(trait,1)):other + 
 idh(at.level(trait,1)):X + idh(at.level(trait,1)):village, 
 rcov=~idh(trait):units, 
 family = cbind("zipoisson","zipoisson","zipoisson","zipoisson","zipoisson"), 
 prior=prior_overdisp_i, 
 data = data, nitt = nitt.1, thin = 50, burnin = 15000, pr = TRUE, pl = TRUE, verbose = TRUE, DIC  = TRUE)

我在这里有两个编程问题:

I have two programming questions here:

1. 如何为此模型指定先验条件?我看了这篇文章中提到的材料，但无法弄清楚.

1. How do I specify a prior for this model? I've looked at the materials mentioned in this post but just can't figure it out.

我运行的相似版本只有两个响应变量，但是我只有一个斜率-我以为每个resp变量都应该有一个不同的斜率.我要去哪里错了，或者我误解了模型?

I've run a similar version with only two response variables, but I only get one slope - where I thought I should get a different slope for each resp variable. Where am I going wrong, or having I misunderstood the model?

推荐答案

基于HLP帖子以及大学/统计顾问的一些帮助，回答了我的第一个问题:

Answer to my first question, based on the HLP post and some help from a colleage/stats consultant:

# values for prior
k <- 5 # originally: length(levels(dative$SemanticClass)), so k = # of outcomes for SemanticClass     aka categorical outcomes 
I <- diag(k-1) #should make matrix of 0's with diagonal of 1's, dimensions k-1 rows and k-1 columns
J <- matrix(rep(1, (k-1)^2), c(k-1, k-1)) # should make k-1 x k-1 matrix of 1's 

对于我的模型，使用multinomial5系列和5个结果变量，先验是:

And for my model, using the multinomial5 family and 5 outcome variables, the prior is:

prior = list(
             R = list(fix=1, V=0.5 * (I + J), n = 4),
             G = list(
               G1 = list(V = diag(4), n = 4))

对于第二个问题，我需要在此模型的固定效果中添加一个交互项:

For my second question, I need to add an interaction term to the fixed effects in this model:

 m <- MCMCglmm(cbind(Resp1, Resp2...) ~ -1 + trait*predictorvariable,
 ...

结果既给出了响应变量的主要效果，又给出了响应/预测变量交互作用的后验估计值(预测变量对每个响应变量的影响).

The result gives both main effects for the Response variables and posterior estimates for the Response/Predictor interaction (the effect of the predictor variable on each response variable).

这篇关于R中的MCMCglmm多项式模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！