glmer-使用二项式数据预测(结合计数数据) [英] glmer - predict with binomial data (cbind count data)

问题描述

我正在尝试针对基于二项式数据运行的glmer模型随时间(x轴上的天数)预测值. Total Alive和Total Dead是计数数据.这是我的模型，以及下面的相应步骤.

I am trying to predict values over time (Days in x axis) for a glmer model that was run on my binomial data. Total Alive and Total Dead are count data. This is my model, and the corresponding steps below.

full.model.dredge<-glmer(cbind(Total.Alive,Total.Dead)~(CO2.Treatment+Lime.Treatment+Day)^3+(Day|Container)+(1|index),
                         data=Survival.data,family="binomial")

您已经在代码(1:index)中看到了过度分散的原因.

We have accounted for overdispersion as you can see in the code (1:index).

然后我们使用dredge命令确定具有主要效果(CO2.处理，Lime.处理，日)及其对应交互的最佳拟合模型.

We then use the dredge command to determine the best fitted models with the main effects (CO2.Treatment, Lime.Treatment, Day) and their corresponding interactions.

dredge.models<-dredge(full.model.dredge,trace=FALSE,rank="AICc")

然后为他们创建一个工作区变量

Then made a workspace variable for them

my.dredge.models<-get.models(dredge.models)

然后我们进行了模型平均，以对最佳拟合模型的系数进行平均

We then conducted a model average to average the coefficients for the best fit models

silly<-model.avg(my.dredge.models,subset=delta<10)

但是，现在我想创建一个图形，Y轴上的总活动量"，X轴上的天数"，以及一条取决于模型输出的拟合线.我知道这很棘手，因为该模型将Total.Alive和Total.Dead串联在一起(请参见模型中的cbind(Total.Alive,Total.Dead).

But now I want to create a graph, with the Total Alive on the Y axis, and Days on the X axis, and a fitted line depending on the output of the model. I understand this is tricky because the model concatenated the Total.Alive and Total.Dead (see cbind(Total.Alive,Total.Dead) in the model.

当我尝试运行预测命令时，我得到了错误

When I try to run a predict command I get the error

# 9: In UseMethod("predict") :
#   no applicable method for 'predict' applied to an object of class "mer"

推荐答案

大多数问题是您使用的是lme4的1.0之前版本，而该版本未实现predict方法. (更新将是最简单的，但是我认为，如果由于某些原因您不能这样做，请在 http://glmm.wikidot.com/faq 可以通过提取固定效果设计矩阵和系数来手动进行预测...)预测实际上没有问题，可以预测对数奇数(默认情况下) )或概率(如果type="response")；如果要预测数字，则必须适当地乘以N.

Most of your problem is that you're using a pre-1.0 version of lme4, which doesn't have the predict method implemented. (Updating would be easiest, but I believe that if you can't for some reason, there's a recipe at http://glmm.wikidot.com/faq for doing the predictions by hand by extracting the fixed-effect design matrix and the coefficients ...)There's actually not a problem with the predictions, which predict the log-odds (by default) or the probability (if type="response"); if you wanted to predict numbers, you'd have to multiply by N appropriately.

您没有给出任何示例，但这是一个使用内置cbpp数据集的可重现(虽然有些琐碎)的示例(我确实收到了一些警告消息-no non-missing arguments to max; returning -Inf-但我认为这可能是是因为模型中只有一个非平凡的固定效应参数?)

You didn't give one, but here's a reproducible (albeit somewhat trivial) example using the built-in cbpp data set (I do get some warning messages -- no non-missing arguments to max; returning -Inf -- but I think this may be due to the fact that there's only one non-trivial fixed-effect parameter in the model?)

library(lme4)
packageVersion("lme4")  ## 1.1.4, but this should work as long as >1.0.0
library(MuMIn)

以后使用(与ggplot一起)添加比例变量很方便:

It's convenient for later use (with ggplot) to add a variable for the proportion:

cbpp <- transform(cbpp,prop=incidence/size)

适合模型(您也可以使用glmer(prop~..., weights=size, ...))

Fit the model (you could also use glmer(prop~..., weights=size, ...))

gm0 <- glmer(cbind(incidence, size - incidence) ~ period+(1|herd),
           family = binomial, data = cbpp)
dredge.models<-dredge(gm0,trace=FALSE,rank="AICc")
my.dredge.models<-get.models(dredge.models)
silly<-model.avg(my.dredge.models,subset=delta<10)

预测有效:

predict(silly,type="response")

创建情节:

library(ggplot2)
theme_set(theme_bw())  ## cosmetic
g0 <- ggplot(cbpp,aes(period,prop))+
    geom_point(alpha=0.5,aes(size=size))

设置预测框架:

predframe <- data.frame(period=levels(cbpp$period))

在总体水平上预测 (ReForm=NA-在lme4`1.0.5中可能必须为REForm=NA):

Predict at the population level (ReForm=NA -- this may have to be REForm=NA in lme4 `1.0.5):

predframe$prop <- predict(gm0,newdata=predframe,type="response",ReForm=NA)

将其添加到图形中

g0 + geom_point(data=predframe,colour="red")+
    geom_line(data=predframe,colour="red",aes(group=1))

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