Alternatively, one can convert dat to a data frame first (?lm states that data
is "an optional data frame containing the variables in the model."):
dat<-matrix(c(0,0,10,20),2,byrow=T)
dat <- as.data.frame(dat)
lm1<-lm(V2~V1, dat)
predict(lm1, data.frame(V1=seq(0,10,1)))
1 2 3 4 5 6 7 8 9 10 11
0 2 4 6 8 10 12 14 16 18 20
Cheers .........
Peter Alspach
>>> Gabor Grothendieck <ggrothendieck at gmail.com> 01/06/05
09:00:15 >>>
On 5/31/05, sms13+ at pitt.edu <sms13+ at pitt.edu>
wrote:> Excuse the simple question...
> I'm not sure what I'm doing wrong with predict, but let me use this
example:
> Suppose I do:
> dat<-matrix(c(0,0,10,20),2,byrow=T)
> lm1<-lm(dat[,2]~dat[,1])
>
> Suppose I want to generate the linearly-interpolated y-values between the
> point (0,0) and (0,20) at every unit interval.
> I thought I just do:
> predict(lm1, data.frame(seq(0,10,1))) to get 0,2,4,6...,18,20, but instead
> I just get:
> 1 2
> 0 20
>
I think the names are confusing it. Try:
x <- dat[,1]; y <- dat[,2]
lm1 <- lm(y ~ x)
predict(lm1, data.frame(x = 1:10))
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