5

回帰モデルの結果から、カテゴリ変数 (4 レベル) と標準化された連続変数の交互作用の勾配の違いまたは類似度を視覚的に表示する交互作用プロットを作成したいと考えています。

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。連続変数の値ごとに勾配が変化するプロットが作成されます。次のプロットのように、勾配が一定のプロットを作成しようとしています。

ここに画像の説明を入力

何か案は?

フォームのモデルに適合しますfit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson) ここにいくつかのサンプルデータがあります:

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", 
"B", "C", "D"), class = "factor"), cont.var = c(-0.0682900527296927, 
0.546320421837542, -0.273160210918771, -0.887770685486005, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.682900527296927, 
0.136580105459385, 0.75119058002662, 0.75119058002662, 0.75119058002662, 
0.341450263648464, 0.75119058002662, 0.546320421837542, 0.546320421837542, 
-0.478030369107849, -0.478030369107849, -0.682900527296927, -0.682900527296927, 
0.546320421837542, -0.478030369107849, -0.0682900527296927, 0.136580105459385, 
0.136580105459385, 0.75119058002662, -0.478030369107849, 0.75119058002662, 
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-0.682900527296927, -0.478030369107849, 0.341450263648464, -0.478030369107849, 
0.546320421837542, 0.75119058002662, -0.478030369107849, -0.273160210918771, 
0.546320421837542, -0.682900527296927, 0.75119058002662, -0.478030369107849, 
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-0.273160210918771, -0.682900527296927, -0.887770685486005, -0.0682900527296927, 
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-0.682900527296927, -0.478030369107849, -0.478030369107849, -0.682900527296927, 
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-0.478030369107849, 0.136580105459385, -0.887770685486005, -0.273160210918771, 
-0.0682900527296927, -0.273160210918771, -0.887770685486005, 
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-0.0682900527296927, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
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-0.273160210918771, -0.273160210918771, -0.273160210918771, 0.75119058002662, 
-0.887770685486005, -0.887770685486005, -0.0682900527296927, 
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0.136580105459385, -0.478030369107849, -0.273160210918771, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, 0.136580105459385, -0.273160210918771, -0.0682900527296927, 
0.75119058002662, 0.136580105459385), resp.var = c(2L, 1L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 1L, 3L, 1L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 
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0L, 3L, 2L, 0L, 2L, 2L, 0L, 0L, 0L, 1L, 1L, 3L, 1L, 2L, 0L, 1L, 
0L, 0L, 1L, 0L, 2L, 0L, 2L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 2L, 0L, 1L, 0L, 4L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 1L, 3L, 0L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 
0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 0L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 
1L, 2L, 0L, 2L, 0L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 6L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
1L, 0L, 0L, 1L, 3L, 1L, 0L, 2L, 3L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 1L, 
1L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 3L, 0L, 0L, 3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
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, 
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0L, 1L, 3L, 0L, 2L, 0L, 0L, 1L, 3L, 1L, 0L, 0L, 4L, 3L, 0L, 2L, 
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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, 
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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, 
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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, 
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43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
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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")
4

3 に答える 3

16

これがある種の答えです(ちなみに、上記のデータフレームに引用符がいくつかありませんでした。これは手動で修正する必要がありました...)

モデルを適合させる:

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cont.var==0。すべてのカテゴリが で同じ値を持つように強制しcont.var*cat.varます。

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

クイックでダーティな線形回帰:

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

ポアソン GLM を使用して (ランダム効果を組み込んでいません)、データ ポイントを表示します。

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")

次のビットでは、メソッドlme4を持つの開発 (r-forge) バージョンが必要です。predict

予測用のデータ 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)))

母集団レベル ( REform=NA) で、線形予測子 (ロジット) スケールで予測します (これは、プロットで直線を取得する唯一の方法です)

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))

ここに画像の説明を入力 次に、応答スケールで:

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))

ここに画像の説明を入力

于 2012-05-05T14:03:05.237 に答える