All you need is predict(fit, data.frame(x)) or if you had started with
a data frame:
xy <- data.frame(x, y)
fit <- lm(y~x, xy)
predict(fit, xy)
David
Professor Emeritus of Anthropology
Texas A&M University
College Station, TX
On Fri, Nov 19, 2021 at 8:45 PM Rolf Turner wrote:
>
> On Fri, 19 N
On Fri, 19 Nov 2021 18:35:23 -0800
Bert Gunter wrote:
> ?predict.lm says:
>
> "predict.lm produces predicted values, obtained by evaluating the
> regression function in the frame newdata (which defaults to
> model.frame(object)). "
>
> model.frame(fit) is:
> 1 1.37095845 -0.30663859
> 2 -0
?predict.lm says:
"predict.lm produces predicted values, obtained by evaluating the
regression function in the frame newdata (which defaults to
model.frame(object)). "
model.frame(fit) is:
1 1.37095845 -0.30663859
2 -0.56469817 -1.78130843
4 0.63286260 1.21467470
6 -0.10612452 -0.43046913
Consider the following toy example:
set.seed(42)
y <- rnorm(20)
x <- rnorm(20)
y[c(3,5,14,15)] <- NA
fit <- lm(y~x)
predict(fit)
This for some reason, which escapes me, does not provide predicted
values when the response/dependent variable is missing, despite
there being
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