Message-ID: <208B2BA3-FFAA-4C2E-BDB1-D05F6A8E9F04@auckland.ac.nz>
Date: 2008-03-10T19:38:18Z
From: Rolf Turner
Subject: ML Estimation Differences with R and SAS
In-Reply-To: <000001c882d1$73288430$59798c90$@net>
On 11/03/2008, at 6:09 AM, Patrick Richardson wrote:
> List,
>
> I'm working on fitting a logistic model for a well known dataset
> (which is
> given below in case anyone wants to try to reproduce). I used both
> R and
> SAS to fit the model and have some differences in the parameter
> estimates.
> I'm wondering if R calculates the ML estimates differently. I'm
> making NO
> accusations as to which program is "right or wrong". That is not
> the focus
> of this posting. As a "newer" R user I'm trying to understand the
> algorithm
> that R might use to calculate ML estimation. The largest
> difference seems
> to with the race factors. R gives a p-value of 0.46995 for
> race=black and
> SAS gives a p-value of 0.0753 for race=black. Clearly one is
> borderline
> significant and the other is not. Many thanks to all who might be
> able to
> offer any insight on this. Both R and SAS code and output are
> included in
> this message (along with the dataset).
Try setting
options(contrasts=c("contr.SAS","contr.poly"))
before you run your analysis in R.
cheers,
Rolf Turner
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