Hi all,
Suppose:
y<-rnorm(100)
x1<-rnorm(100)
lm.yx<-lm(y~x1)
To predict from a new data source, one can use:
# works as expected
dum<-data.frame(x1=rnorm(200))
predict(lm.yx, newdata=dum)
Suppose lm.yx has been run and we have the lm object. And we have a
dataframe that has columns that don't correspond by name to the
original regressors. I very! naively assumed that doing this (below)
would work. It does not.
# does not work
lm.yx$coefficients<-c("Intercept", "n.x1")
dum2<-data.frame(Int=rep(1,200), n.x1=rnorm(200))
predict(lm.yx, newdata=dum2)
I know that a simple alternative is to do:
# because we messed around with the lm object above, re-building
lm.yx<-lm(y~x1)
# change names of dum2 to match names of coefficients of lm.yx
names(dum2)<-names(coefficients(lm.yx))
predict(lm.yx, newdata=dum2)
Is there another way that involves changing the lm object rather than
changing the prediction data.frame?
Thanks,
Anirban
--
Anirban Mukherjee | Assistant Professor, Marketing
LKCSB, Singapore Management University
5056 School of Business, 50 Stamford Road
Singapore 178899 | +65-6828-1932
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