On 6/2/12 19:36 , peter dalgaard wrote:
Actually, I think the issue is slightly different: IDL assumes that
the errors _are_ something (notice that setting measure_errors to 1
is not equvalent to omitting them), R assumes that they are
_proportional_ to the inverse weights
Yes, I think this is correct.
There are a couple of ways to avoid the use of the estimated
multiplicative dispersion parameter in R, one is to extract
cov.unscaled from the summary,
Thanks, this appears to give me what I am looking for. In fact it seems
that cov and cov.unscaled give identical results which *do* account for
the weights in each case. The default seems to be weights = 1 (ie, an
actual measurement error of 1). Anyway, this now reconciles with what I
was used to with IDL.
eg:
> summary(lm(y~x))$cov
(Intercept) x
(Intercept) 1.5 -0.5
x -0.5 0.2
> summary(lm(y~x))$cov.unscaled
(Intercept) x
(Intercept) 1.5 -0.5
x -0.5 0.2
> summary(lm(y~x,weights=c(10,10,10,10)))$cov
(Intercept) x
(Intercept) 0.15 -0.05
x -0.05 0.02
> summary(lm(y~x,weights=c(10,10,10,10)))$cov.unscaled
(Intercept) x
(Intercept) 0.15 -0.05
x -0.05 0.02
> summary(lm(y~x,weights=c(10,10,1,1)))$cov.unscaled
(Intercept) x
(Intercept) 0.2669039 -0.13167260
x -0.1316726 0.07829181
> summary(lm(y~x,weights=c(10,10,1,1)))$cov
(Intercept) x
(Intercept) 0.2669039 -0.13167260
x -0.1316726 0.07829181
and the last case in IDL:
IDL>
vec=linfit(x,y,sigma=sig,measure_errors=[sqrt(.1),sqrt(.1),1,1],covar=cov)
IDL> print,cov
0.266904 -0.131673
-0.131673 0.0782918
(the other parameters also agree!)
--
James D Annan jdan...@jamstec.go.jp Tel: +81-45-778-5618 (Fax 5707)
Senior Scientist, Research Institute for Global Change, JAMSTEC
Yokohama Institute for Earth Sciences, 3173-25 Showamachi,
Kanazawa-ku, Yokohama City, Kanagawa, 236-0001 Japan
http://www.jamstec.go.jp/frcgc/research/d5/jdannan/
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