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RE: st: conditional SE of y|X in glm
From
Nick Cox <[email protected]>
To
"'[email protected]'" <[email protected]>
Subject
RE: st: conditional SE of y|X in glm
Date
Tue, 24 Apr 2012 12:29:20 +0100
With this model, as with every other, you have to decide what you mean by "prediction", i.e. on what scale you are predicting.
Also, I did write
"I like to have such measures accessible for comparing -glm- results with those of other models in which rmse appears naturally."
and I think logit models are stretching the point.
In essence, what -glmcorr- does in your example is either wrong or irrelevant, depending on your point of view. -glmcorr- can be reconciled with those results by doing instead
. gen fraction = r/n
. glm fraction ldose , link(logit)
Iteration 0: log likelihood = 3.345982
Iteration 1: log likelihood = 3.7166249
Iteration 2: log likelihood = 3.7245648
Iteration 3: log likelihood = 3.724566
Iteration 4: log likelihood = 3.724566
Generalized linear models No. of obs = 24
Optimization : ML Residual df = 22
Scale parameter = .0468293
Deviance = 1.030244611 (1/df) Deviance = .0468293
Pearson = 1.030244611 (1/df) Pearson = .0468293
Variance function: V(u) = 1 [Gaussian]
Link function : g(u) = ln(u/(1-u)) [Logit]
AIC = -.1437138
Log likelihood = 3.724566043 BIC = -68.88694
------------------------------------------------------------------------------
| OIM
fraction | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ldose | 22.43087 5.627079 3.99 0.000 11.402 33.45974
_cons | -40.34087 10.10823 -3.99 0.000 -60.15264 -20.52909
------------------------------------------------------------------------------
. glmcorr
fraction and predicted
Correlation 0.800
R-squared 0.640
Root MSE 0.216
. di sqrt(e(dispers))
.21640079
However, that would lose some of the information in the data.
Otherwise, -glmcorr- uses what -predict- produces by default; if that's wrong for your problem, so will the results be.
Nick
[email protected]
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Marco Ventura
Sent: 24 April 2012 10:29
To: [email protected]
Subject: Re: st: conditional SE of y|X in glm
Dear Nick,
thank you very much of your quick replies.
Unfortunately there is something I still do not understand. If I do
use http://www.stata-press.com/data/r10/beetle
glm r ldose, fam(bin n) link (logit)
di sqrt(e(dispers))
glmcorr
I get two very different values 4.065 against 13.179. Which of the two
is correct?
Thank you again.
Marco
Il 24/04/2012 10:57, Nick Cox ha scritto:
> See -glmcorr- (SSC) for one approach here. That calculates an rmse
> which appears similar, if not identical, to what you want. I like to
> have such measures accessible for comparing -glm- results with those
> of other models in which rmse appears naturally. Perhaps it is a
> comfort blanket, but there you go.
>
> Note that putting a constant into a variable is usually overkill as
>
> di sqrt(e(dispers))
>
> does the calculation. Use a scalar or local macro if you want to store
> the value.
>
> On Tue, Apr 24, 2012 at 9:31 AM, Marco Ventura<[email protected]> wrote:
>
>> from a GLM estimate I want to retrieve the conditional standard error of y
>> given the covariates. If I do
>>
>> gen sigma=sqrt(e(dispers))
>>
>> do I always get the right thing independently of any family and link?
>> Should I correct it by sqrt(e(dispers)* (_N-1)/_N)?
>> And do you think I should instead use the Pearson residuals such as
>>
>> gen sigma=sqrt(e(dispers_p))
>>
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