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Re: st: Fw: influential observations


From   Arti Pandey <rtpandey@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Fw: influential observations
Date   Tue, 12 Apr 2011 02:56:33 -0700 (PDT)

Thanks Nick. I "think" I see what you mean, with the river example. My lack of 
understanding stems from automation, using software that would do everything and 
all we were looking at was the chi2 values. The field being x-ray 
crystallography, there were several hundred thousand observations, and there 
were always some that had been excluded at the end of convergence of data. I 
never worried about it.
What I am dealing with now is very few (89, and precious) observations, which 
are clinical measurements. So if a statistical software tells me to look at 
certain observations, I do that, find they are all right, no reason to exclude 
and hit a dead end.
For the inverse link, the glm goes on endlessly with iterations, I guess because 
I have zeroes in my response variable.  
Arti


----- Original Message ----
From: Nick Cox <njcoxstata@gmail.com>
To: statalist@hsphsun2.harvard.edu
Sent: Tue, April 12, 2011 2:43:55 PM
Subject: Re: st: Fw: influential observations

Do Anscombe residuals come out normal with non-normal families? I am
away from any pertinent literature.

On Tue, Apr 12, 2011 at 8:51 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> I don't think it is a good idea to expect a firm statistical answer
> based on this information.
>
> 1. Isn't there science that will throw light on this question for you?
> For example, in my field, the Amazon is often an influential
> observationr, as are other very big rivers. But throwing them out just
> because they might make modelling awkward would usually be very
> strange science. They deserve their votes. Your field, whatever it is,
> wil presumably have its own arguments and issues.
>
> 2. When there are influential observations in a -glm-, considering a
> different link, e.g. reciprocal, is often a good way forward.
>
> 3. There are many situations in which one predictor that is
> insignificant at conventional levels deserves its place in a model if
> it has a logical role.
>
> 4. I don't see why you expect normally distributed residuals when the
> family is gamma!!! I think that overall plots of residual vs fitted,
> observed vs fitted, variance of residual vs fitted, etc., are worth
> more attention than the marginal distribution.
>
> Nick
>
> On Tue, Apr 12, 2011 at 6:17 AM, Arti Pandey <rtpandey@yahoo.com> wrote:
>> Hello
>> A belated thank you to Maarten Buis and David Greenberg for suggestions to my
>> previous query.
>> I decided to go with -glm- for my model and have been trying to understand 
the
>> different procedures for checking the model
>> The anscombe residuals and deviance are  normally distributed, but there are
>> three influential observations based upon cooksd.
>> On removing these observations, the BIC rises by 10, and one of the 
predictors
>> also becomes insignificant.
>> Is the model fitting because of these influential observations now and 
>>therefore
>>
>> not correct?
>> I have continuous response data and used gamma distribution with log link.
>> Any recommendations for information on model checking after glm are also
>> appreciated, the book "glm
>>
>> and extensions" by Hardin and Hilbe is out of my reach, unless an electronic
>> copy is available.
>

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