如何绘制两个固定类别值的比较以对另一个连续变量进行线性回归 [英] How to plot a comparisson of two fixed categorical values for linear regression of another continuous variable

问题描述

所以我想画这个图:

lmfit = lm(y〜a + b)

lmfit = lm (y ~ a + b)

但是，"b"只有零和一的值.因此，我想绘制两条单独的回归线，它们彼此平行，以显示b对y截距的影响.因此，在绘制此图形之后:

but, "b" only has the values of zero and one. So, I want to plot two separate regression lines, that are paralel to one one another to show the difference that b makes to the y-intercept. So after plotting this:

图(b，y)

plot(b,y)

然后我想使用abline(lmfit,col="red",lwd=2)两次，一次将b的x值设置为零，一次将其设置为1.因此，一次不包含该术语，一次b仅为1b.

I want to then use abline(lmfit,col="red",lwd=2) twice, once with the x value of b set to zero, and once with it set to one. So once without the term included, and once where b is just 1b.

要重述:b是绝对值，0或1.a是连续的，具有轻微的线性趋势.

To restate: b is categorical, 0 or 1. a is continuous with a slight linear trend.

谢谢.

示例:

推荐答案

您可能要考虑将predict(...)与b=0和b=1一起使用，如下所示.由于您未提供任何数据，因此我正在使用内置的mtcars数据集.

You might want to consider using predict(...) with b=0 and b=1, as follows. Since you didn't provide any data, I'm using the built-in mtcars dataset.

lmfit   <- lm(mpg~wt+cyl,mtcars)
plot(mpg~wt,mtcars,col=mtcars$cyl,pch=20)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=4)),col=4,add=T)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=6)),col=6,add=T)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=8)),col=8,add=T)

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