Whats the R equivalent for Proc logistic in SAS ? Is there a stepwise
method there ?
How to create scoring models in R , for larger datasets (200 mb), Is
there a way to compress and use datasets (like options compress=yes;)
Ajay
On Wed, Sep 10, 2008 at 11:12 AM, Peter Dalgaard
<[EMAIL PROTECTED]> wrote:
> Rolf Turner wrote:
>>
>> For one thing your call to glm() is wrong --- didn't you notice the
>> warning messages about ``non-integer #successes in a binomial glm!''?
>>
>> You need to do either:
>>
>> glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data,
>> offset=log(y), weights=k)
>>
>> or:
>>
>> glm(cbind(r,k-r) ~ x, family=binomial(link='cloglog'), data=bin_data,
>> offset=log(y))
>>
>> You get the same answer with either, but this answer still does not agree
>> with your
>> SAS results. Perhaps you have an error in your SAS syntax as well. I
>> wouldn't know.
>
> The data created in the data step are not those used in the analysis.
> Changing to
>
> data nelson;
> <etc>
>
> gives the same result as R on the versions I have available:
>
> Analysis Of Parameter
> Estimates
>
> Standard Wald 95%
> Confidence Chi-
> Parameter DF Estimate Error Limits
> Square Pr > ChiSq
>
> Intercept 1 -3.5866 2.2413 -7.9795
> 0.8064 2.56 0.1096
> x 1 0.9544 2.8362 -4.6046
> 6.5133 0.11 0.7365
> Scale 0 1.0000 0.0000 1.0000
> 1.0000
>
> and
> Call:
> glm(formula = r/k ~ x, family = binomial(link = "cloglog"), data = bin_data,
> weights = k, offset = log(y))
>
> Deviance Residuals: 1 2 3 4 0.5407 -0.9448
> -1.0727 0.7585
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -3.5866 2.2413 -1.600 0.110
> x 0.9544 2.8362 0.336 0.736
>
>
>>
>> cheers,
>>
>> Rolf Turner
>>
>> On 10/09/2008, at 10:37 AM, sandsky wrote:
>>
>>>
>>> Hello,
>>>
>>> I have different results from these two softwares for a simple binomial
>>> GLM
>>> problem.
>>>>
>>>> From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59,
>>>
>>> coeff(x)=0.95
>>>>
>>>> From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99,
>>>> coeff(x)=1.36
>>>
>>> Is there anyone tell me what I did wrong?
>>>
>>> Here are the code and results,
>>>
>>> 1) SAS Genmod:
>>>
>>> % r: # of failure
>>> % k: size of a risk set
>>>
>>> data bin_data;
>>> input r k y x;
>>> os=log(y);
>>> cards;
>>> 1 3 5 0.5
>>> 0 2 5 0.5
>>> 0 2 4 1.0
>>> 1 2 4 1.0
>>> ;
>>> proc genmod data=nelson;
>>> model r/k = x / dist = binomial link =cloglog offset = os ;
>>>
>>> <Results from SAS>
>>>
>>> Log Likelihood -4.7514
>>>
>>> Parameter DF Estimate Error Limits
>>> Square Pr > ChiSq
>>>
>>> Intercept 1 -3.6652 1.9875 -7.5605 0.2302
>>> 3.40 0.0652
>>> x 1 0.8926 2.4900 -3.9877 5.7728
>>> 0.13 0.7200
>>> Scale 0 1.0000 0.0000 1.0000 1.0000
>>>
>>>
>>>
>>> 2) glm in R
>>>
>>> bin_data <-
>>>
>>> data.frame(cbind(y=c(5,5,4,4),r=c(1,0,0,1),k=c(3,2,2,2),x=c(0.5,0.5,1.0,1.0)))
>>> glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data,
>>> offset=log(y))
>>>
>>> <Results from R>
>>> Coefficients:
>>> (Intercept) x
>>> -3.991 1.358
>>>
>>> 'log Lik.' -0.9400073 (df=2)
>>
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>
>
> --
> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
> ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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>
--
Regards,
Ajay Ohri
http://tinyurl.com/liajayohri
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