[R] different outcomes of P values in SPSS and R

[Ben Bolker]

  Yes, but ... the original poster said the coefficients differed too.
  (The blog post
you refer to deals with ANOVA (i.e. linear models) rather than GLMs (generalized
linear models): it is true that the sequential/marginal
distinction still applies, but I don't think that can be the *only*
thing going on here.)


On Fri, Aug 13, 2010 at 10:50 PM, Martin Teicher
<martin_teicher at hms.harvard.edu> wrote:
> R usesType I sequential SS, not the default Type III marginal SS reported by SPSS.  There is a good blog post explaining this difference along with some interesting comments -- http://myowelt.blogspot.com/2008/05/obtaining-same-anova-results-in-r-as-in.html
>
> Best Wishes,
>
> Martin H. Teicher
> Dept of Psychiatry
> McLean Hospital / Harvard Medical School
> Belmont MA 02478
>
>
> On Aug 13, 2010, at 10:32 PM, Ben Bolker wrote:
>
>> Leo Vorthoren <L.Vorthoren <at> nioo.knaw.nl> writes:
>>
>>> I have been using generalized linear models in SPSS 18, in order to build
>>> models and to calculate the P values. When I was building models in Excel
>>> (using the intercept and Bs from SPSS), I noticed that the graphs differed
>>> from my expectations. When I ran the dataset again in R, I got totally
>>> different outcomes for both the P values as well as the Bs and the
>>> intercepts. The outcomes of R seem much more likely to be the correct ones,
>>> but I really cannot explain the differences.
>>
>>  I appreciate/assume that you're asking on the off chance that someone
>> else has tried something very similar and gone to the trouble of figuring
>> out the differences between R's and SPSS's default setup, but you're
>> unlikely to get an answer without more detailed information.
>>
>>  My best guess is that SPSS and R are using different contrasts
>> and/or different baseline levels.  R uses treatment contrasts by default,
>> and assumes that the first (alphabetical) level of a factor is the
>> baseline level.
>>
>>  It's conceivable that you have a dataset where the results are
>> numerically unstable and sensitive to small details in the algorithms
>> used.
>>
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>
>