从线性模型绘制交互效果的最佳方法

In an effort to help populate the R tag here, I am posting a few questions I have often received from students. I have developed my own answers to these over the years, but perhaps there are better ways floating around that I don't know about.

为了帮助填充R标签，我发布了一些我经常从学生那里收到的问题。多年来，我已经对这些做出了自己的答案，但也许还有更好的方法，我不知道。

The question: I just ran a regression with continuous y and x but factor f (where levels(f) produces c("level1","level2"))

问题：我只是连续y和x但是因子f运行回归（其中level（f）产生c（“level1”，“level2”））

 thelm <- lm(y~x*f,data=thedata)

Now I would like to plot the predicted values of y by x broken down by groups defined by f. All of the plots I get are ugly and show too many lines.

现在我想绘制y由x定义的组的x的预测值。我得到的所有情节都很丑陋而且显示的线条太多了。

My answer: Try the predict() function.

我的回答：尝试使用predict（）函数。

##restrict prediction to the valid data 
##from the model by using thelm$model rather than thedata

 thedata$yhat <- predict(thelm,
      newdata=expand.grid(x=range(thelm$model$x),
                          f=levels(thelm$model$f)))

 plot(yhat~x,data=thethedata,subset=f=="level1")
 lines(yhat~x,data=thedata,subset=f=="level2")

Are there other ideas out there that are (1) easier to understand for a newcomer and/or (2) better from some other perspective?

是否有其他想法（1）对于新手更容易理解和/或（2）从其他角度更好？

4 个解决方案

#1

The effects package has good ploting methods for visualizing the predicted values of regressions.

效果包具有良好的绘图方法，用于可视化回归的预测值。

thedata<-data.frame(x=rnorm(20),f=rep(c("level1","level2"),10))
thedata$y<-rnorm(20,,3)+thedata$x*(as.numeric(thedata$f)-1)

library(effects)
model.lm <- lm(formula=y ~ x*f,data=thedata)
plot(effect(term="x:f",mod=model.lm,default.levels=20),multiline=TRUE)

#2

Huh - still trying to wrap my brain around expand.grid(). Just for comparison's sake, this is how I'd do it (using ggplot2):

嗯 - 仍然试图围绕expand.grid（）包裹我的大脑。仅仅为了比较，我就是这样做的（使用ggplot2）：

thedata <- data.frame(predict(thelm), thelm$model$x, thelm$model$f)

ggplot(thedata, aes(x = x, y = yhat, group = f, color = f)) + geom_line()

The ggplot() logic is pretty intuitive, I think - group and color the lines by f. With increasing numbers of groups, not having to specify a layer for each is increasingly helpful.

我认为ggplot（）逻辑非常直观 - 用f对行进行分组和着色。随着组数量的增加，不必为每个组指定一个层越来越有用。

#3

I am no expert in R. But I use:

我不是R.的专家。但我使用：

xyplot(y ~ x, groups= f, data= Dat, type= c('p','r'), 
   grid= T, lwd= 3, auto.key= T,)

This is also an option:

这也是一个选择：

interaction.plot(f,x,y, type="b", col=c(1:3), 
             leg.bty="0", leg.bg="beige", lwd=1, pch=c(18,24), 
             xlab="", 
             ylab="",
             trace.label="",
             main="Interaction Plot")

#4

Here is a small change to the excellent suggestion by Matt and a solution similar to Helgi but with ggplot. Only difference from above is that I have used the geom_smooth(method='lm) which plots regression lines directly.

这是对马特的优秀建议和类似于Helgi的解决方案的一个小改动，但是使用了ggplot。与上面的区别仅在于我使用了geom_smooth（method ='lm），它直接绘制了回归线。

set.seed(1)
y = runif(100,1,10)
x = runif(100,1,10)
f = rep(c('level 1','level 2'),50)
thedata = data.frame(x,y,f)
library(ggplot2)
ggplot(thedata,aes(x=x,y=y,color=f))+geom_smooth(method='lm',se=F)

#1