如何根据线性模型在r中创建置信区间？

Question

I want to create a linear model of y vs x and use it to find a confidence interval for y given a observed value of x.

So I created some samples:

x=rnorm(100,2)
y=rnorm(100,2)

and created the linear model:

bbbb=lm(y~x)

But when I use predict to create the confidence intervals, it gives me a bunch list of confidence intervals rather than a single one?

predict(bbbb,x=2,interval="confidence")

returns:

...
51  2.188294 1.949615 2.426973
52  2.189329 1.932474 2.446183
53  2.176816 1.950111 2.403521
54  2.183961 1.998136 2.369786
...

How can I make it return just one confidence interval for y when x=2?

Answer 1

You need:

predict(bbbb, newdata = data.frame(x = 2), interval  = "confidence")

for reasons that are clear when you look at ?predict.lm .

The newdata argument is required to be a data frame. You supplied a length 1 vector. Secondly, because of the way S3 methods work, what you thought you were passing to newdata was actually not passed to newdata at all. You actually specified a new argument x with value 2 , which predict() promptly forgot about (it got mopped up by the ... argument). If newdata is not supplied, predict() uses the data stored in the fitted model object ( bbbb ) and returns the fitted values plus any requested extras like CI, standard errors etc.