Since you passed a matrix to lm() and then a data.frame to predict(), predict
can't match up what variables to use for the prediction so it falls back on the
original data. This seems to work:
> set.seed(42)
> y <- rnorm(100)
> X <- matrix(rnorm(100*10), ncol=10)
> Xd <- data.frame(X)
> lm <- lm(y~., Xd)
> Xnew <- matrix(rnorm(100*20), ncol=10)
> Xnewd <- data.frame(Xnew)
> ynew <- predict(lm, newdata=Xnewd)
> head(ynew)
1 2 3 4 5 6
0.35404067 0.14073495 -0.45442499 0.31065562 -0.02091366 0.25358175
> head(predict(lm))
1 2 3 4 5 6
0.75474817 0.06024122 -0.27221466 -0.20344713 0.20218135 -0.24045859
>
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Martin Spindler
Sent: Wednesday, April 29, 2015 9:21 AM
To: r-help@r-project.org
Subject: [R] Problem with predict.lm()
Dear all,
the following example somehow uses the "old data" (X) to make the predictions,
but not the new data Xnew as intended.
y <- rnorm(100)
X <- matrix(rnorm(100*10), ncol=10)
lm <- lm(y~X)
Xnew <- matrix(rnorm(100*20), ncol=10)
ynew <- predict(lm, newdata=as.data.frame(Xnew)) #prediction in not made for
Xnew
How can I foce predict.lm to use use the new data?
Thank you very much for your efforts in advance!
Best,
Martin
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
