Hi Stephan,
You'll get the expected result if you pass a data.frame to the lm() function
when fitting the model.
> yy <- runif(4)
> XX <- matrix(runif(8),ncol=2)
> df <- data.frame(y=yy, XX)
> df
y X1 X2
1 0.52889284 0.8055476 0.6670006
2 0.09989951 0.2498907 0.8867955
3 0.17523284 0.8959978 0.2316362
4 0.82489564 0.4446880 0.1369342
> model <- lm(y~., data=df)
> fitted(model)
1 2 3 4
0.2442617 0.2634438 0.4734091 0.6478062
> XX.pred <- data.frame(matrix(runif(6), ncol=2))
> names(XX.pred) <- c("X1", "X2")
> predict(model, XX.pred)
1 2 3
0.1365312 0.2394404 0.3789291
Best regards,
Charlie Roosen
Mango Solutions
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Stephan Kolassa
Sent: 17 August 2010 14:25
To: R-help@r-project.org
Subject: [R] predict.lm, matrix in formula and newdata
Dear all,
I am stumped at what should be a painfully easy task: predicting from an lm
object. A toy example would be this:
XX <- matrix(runif(8),ncol=2)
yy <- runif(4)
model <- lm(yy~XX)
XX.pred <- data.frame(matrix(runif(6),ncol=2))
colnames(XX.pred) <- c("XX1","XX2")
predict(model,newdata=XX.pred)
I would have expected the last line to give me the predictions from the model
based on the new data given in XX.pred... but all I get are in-sample fits
along with a warning "'newdata' had 3 rows but variable(s) found have 4 rows".
Why would predict.lm worry about the number of rows in the model matrix?
Unfortunately, ?predict.lm does not seem to be helpful, and neither RSiteSearch
nor rseek.org have been useful. I'm sure that I am making an elementary error
somewhere (am I misunderstanding the lm(yy~XX) part?) and would appreciate a
gentle nudge in the right direction.
Thank you,
Stephan
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