[R] How to plot predicted probabilities with 95% CIs

[Michael Friendly]

Why not use the effects package -- designed for this task.

library(effects)
plot(allEffects(mfit))

?plot.eff   # for details

On 10/3/2016 3:15 AM, Faradj Koliev wrote:
> Dear all,
>
> I need a little help with plotting predicted probabilities (values). Consider the following example
>
> **
> data(”mtcars”)
>
> mfit = lm(mpg ~ vs + disp + cyl, data=mtcars)
>
> newcar=data.frame(vs=c(0,1), disp=230, cyl=6.188)
>
> Pmodel<–predict(mfit, newcar)
> **
>
> I want to plot the effect of ”vs” ( 0 and 1) when all other variables are held constant (mean).
>
> To do this I run this code below:
> **
> plot(1:2, Pmodel$estimates[1:2,1],ylim=c(0,1),pch=19, xlim=c(.5,2.5), xlab=”X", ylab=”Predicted value of Y", xaxt="n", main= ”Predicted value of Y with 95% CIs")
> arrows(1:2, (Pmodel $estimates[1:2,1]-1.96*Pmodel$estimates[1:2,2]), 1:2, (Pmodel$estimates[1:2,1]+1.96*Pmodel$estimates[1:2,2]), length=0.05, angle=90, code=3)
> axis(1,at=c(1,2), labels=c(”Yes”,"No"))
> **
> What am I doing wring here? Thanks!
>
> Best,
> Faradj
> 	[[alternative HTML version deleted]]
>
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