On Fri, 30 Mar 2012, Smit, M.J. wrote:
Dear list members,
I want to do a scenario analysis: run a regression on the real data, then make
some changes to that, and do a prediction based on the changed data. There is a
predict command under sarlm for this, and it works. However, I have two
questions.
- predict(model,newdata=NULL,weights) uses not only trend (the
non-spatial terms) and signal (the spatial "smooth") but also noise (the
residuals from the original regression). Is it true I can avoid this by
explicitly inserting my old dataset into newdata=? The predictions
differ, so something has happened.
Yes, this is what is happening. In the newdata=NULL case, the response is
known, so the signal is defined in terms of the response. If newdata is
given, it is defined in terms of the data generation process.
- predict then gives me a list object, and I'm at a loss how to get the
results from this. I've named the objects pred1 and pred2, and vainly
tried pred1$trend and pred1[[1]], which gives the first observation from
the $trend subvariable, but doesn't allow access to the other
subvariables. Is there a way to get this into as.data.frame?
I'll add an S3 method to do this. For now, assign the result of the print
method to an object:
goAway <- capture.output(predsDataFrame <- print(<sarlm.predObject>))
with capture.output() to prevent it printing to screen. The
as.data.frame() method is in revision 430 on R-forge.
Roger
Best regards,
Martijn
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Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
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