R：GLMM（lme4）の連続変数とカテゴリ変数による相互作用プロット

Question

カテゴリ変数（4レベル）と標準化された変数の相互作用の勾配の差異または類似性を視覚的に表示するための相互作用プロットを作成したいと考えています回帰モデルの結果からの連続変数。

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) 私が探しているものではありません。これは、連続変数の各値に対してスロープが変化するプロットを生成します。私は次のプロットのように一定の勾配を有するプロットを作るために探しています：

任意のアイデア？私はここでは、フォームfit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson) のモデルに適合

は、いくつかのサンプルデータです：

structure(list(cat.var = structure(c(4L, 4L, 1L, 4L, 1L, 2L, 
1L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 2L, 4L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 1L, 3L, 1L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 
3L, 4L, 3L, 4L, 1L, 3L, 3L, 1L, 2L, 3L, 4L, 3L, 4L, 2L, 1L, 1L, 
4L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 4L, 4L, 3L, 3L, 1L, 3L, 3L, 
3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 4L, 
1L, 3L, 1L, 1L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 2L, 4L, 
4L, 1L, 2L, 1L, 4L, 3L, 1L, 1L, 3L, 2L, 4L, 4L, 1L, 4L, 1L, 3L, 
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 
2L, 2L, 1L, 1L, 2L, 3L, 1L, 4L, 4L, 4L, 1L, 4L, 4L, 3L, 2L, 4L, 
1L, 3L, 1L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 3L, 4L, 2L, 1L, 3L, 3L, 
4L, 3L, 2L, 3L, 1L, 4L, 2L, 2L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 
2L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 2L, 1L, 4L, 1L, 2L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 3L, 
3L, 3L, 4L, 1L, 3L, 1L, 1L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 3L, 2L, 
1L, 4L, 4L, 2L, 4L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 2L, 3L, 2L, 4L, 
1L, 1L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 4L, 1L, 4L, 
2L, 4L, 3L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 
4L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 4L, 
1L, 4L, 3L, 1L, 2L, 1L, 4L, 2L, 4L, 4L, 1L, 2L, 1L, 1L, 1L, 4L, 
1L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 4L, 3L, 1L, 4L, 1L, 
2L, 4L, 1L, 1L, 3L, 3L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 1L, 2L, 4L, 
3L, 4L, 4L, 4L, 4L, 1L, 3L, 1L, 2L, 2L, 2L, 4L, 2L, 3L, 4L, 1L, 
3L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 
1L, 1L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 
1L, 3L, 1L, 3L, 1L, 4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 2L, 4L, 
4L, 2L, 3L, 4L, 4L, 3L, 1L, 4L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 
1L, 1L, 1L, 3L, 4L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 1L, 
1L, 4L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 4L, 2L, 
3L, 1L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 1L), .Label = c("A", 
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0L, 2L, 1L, 1L, 0L, 2L, 2L, 0L, 2L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 
3L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 
0L, 3L, 1L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 
0L, 1L, 0L, 0L, 3L, 1L, 1L, 2L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 1L, 
0L, 1L, 3L, 0L, 2L, 0L, 0L, 1L, 3L, 1L, 0L, 0L, 4L, 3L, 0L, 2L, 
0L, 0L, 0L, 3L, 0L, 0L, 2L, 3L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 3L, 3L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 0L, 
2L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 
1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
0L, 0L, 3L, 2L, 2L, 0L, 1L, 0L, 5L, 0L, 4L, 2L, 0L, 3L, 0L, 0L, 
1L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 3L, 0L, 2L, 0L, 0L, 0L, 2L, 
0L), rand.eff = c(37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L)), .Names = c("cat.var", 
"cont.var", "resp.var", "rand.eff"), row.names = c(NA, 500L), class = "data.frame") 

Answer 1

は、ここでの方法によって、あなたが持っていた上記のデータフレームにいくつか欠けている引用符を持っていたある種の答えは（です手動で固定されて...）

は、モデルのフィット：

library(lme4) 
fit <- glmer(resp.var ~ cont.var:cat.var + (1|rand.eff) , 
      data = sample.data , poisson) 

（これは少し奇妙なモデル仕様であることに注意してください - 力すべてのカテゴリは同じ値を持つcont.var==0です。 cont.var*cat.varを意味しましたか？

library(ggplot2) 
theme_update(theme_bw()) ## set white rather than gray background 

間に合わせ線形回帰：

ggplot(sample.data,aes(cont.var,resp.var,linetype=cat.var))+ 
    geom_smooth(method="lm",se=FALSE) 

ポアソンGLMとすぐ

（ランダムな効果を組み込むことではない）、そしてデータポイントを示す：

ggplot(sample.data,aes(cont.var,resp.var,colour=cat.var))+ 
    stat_sum(aes(size=..n..),alpha=0.5)+ 
    geom_smooth(method="glm",family="poisson") 

次のビットpredictメソッドを有するlme4の開発（rフォージ）バージョンが必要：

は、予測のためのデータフレームを設定します。

predframe <- with(sample.data, 
        expand.grid(cat.var=levels(cat.var), 
           cont.var=seq(min(cont.var), 
           max(cont.var),length=51))) 

は

（これはあなたがプロット上の直線を取得する唯一の方法です）集団レベル（ REform=NA）で、線形予測に（ロジット）規模を予測します

predframe$pred <- predict(fit,newdata=predframe,REform=NA,type="response") 
ggplot(predframe,aes(cont.var,pred,linetype=cat.var))+geom_line()+ 
    geom_point(data=subset(predframe,cont.var %in% minmaxvals), 
       aes(shape=cat.var)) 

：応答の規模で今すぐ

predframe$pred.logit <- predict(fit,newdata=predframe,REform=NA) 

minmaxvals <- range(sample.data$cont.var) 

ggplot(predframe,aes(cont.var,pred.logit,linetype=cat.var))+geom_line()+ 
    geom_point(data=subset(predframe,cont.var %in% minmaxvals), 
       aes(shape=cat.var)) 

Answer 2

effectsパッケージはlme4モデルをサポートしており、必要な操作を行うことができます。

効果：線形、一般化線形、および他のモデル

グラフと表効果表示のため、例えば、相互作用の、線形予測子との様々な統計モデルの効果を表示します。

また、少し古い2つのpapersがあります（あなたはそれらをビネットと考えることができます）。

Answer 3

jtoolsパッケージ（CRAN link）は、この種のモデルのプロットをかなり簡単にすることができます。私はそのパッケージの開発者です。

ベンは彼の答えに行ったように私たちはモデルに適合します：

library(lme4) 
fit <- glmer(resp.var ~ cont.var:cat.var + (1 | rand.eff), 
      data = sample.data, family = poisson) 

そしてjtoolsで私達はちょうどこのようinteract_plot機能を使用します。

library(jtools) 
interact_plot(fit, pred = cont.var, modx = cat.var) 

結果：

defそれを応答スケールにプロットしますが、outcome.scale = "link"引数（デフォルトは"response"）を使用してリニアスケールでプロットすることができます。