[R] bigglm() results different from glm()

[utkarshsinghal]

Thank you Mr. Lumley and Mr. Greg. That was helpful.

Regards
Utkarsh



Thomas Lumley wrote:
> On Fri, 3 Jul 2009, utkarshsinghal wrote:
>
>>
>> Hi Sir,
>>
>> Thanks for making package available to us. I am facing few problems 
>> if you can give some hints:
>>
>> Problem-1:
>> The model summary and residual deviance matched (in the mail below) 
>> but I didn't understand why AIC is still different.
>>
>>> AIC(m1)
>> [1] 532965
>>
>>> AIC(m1big_longer)
>> [1] 101442.9
>
> That's because AIC.default uses the unnormalized loglikelihood and 
> AIC.biglm uses the deviance.  Only differences in AIC between models 
> are meaningful, not individual values.
>
>>
>> Problem-2:
>> chunksize argument is there in bigglm but not in biglm, consequently, 
>> udate.biglm is there, but not update.bigglm
>> Is my observation correct? If yes, why is this difference?
>>
>
> Because update.bigglm is impossible.
>
> Fitting a glm requires iteration, which means that it requires 
> multiple passes through the data. Fitting a linear model requires only 
> a single pass. update.biglm can take a fitted or partially fitted 
> biglm and add more data. To do the same thing for a bigglm you would 
> need to start over again from the beginning of the data set.
>
> To fit a glm, you need to specify a data source that bigglm() can 
> iterate over.  You do this with a function that can be called 
> repeatedly to return the next chunk of data.
>
>       -thomas
>
> Thomas Lumley            Assoc. Professor, Biostatistics
> tlumley at u.washington.edu    University of Washington, Seattle
>
>
>
>

I don't know why the AIC is different, but remember that there are multiple definitions for AIC (generally differing in the constant added) and it may just be a difference in the constant, or it could be that you have not fit the whole dataset (based on your other question).

For an lm model biglm only needs to make a single pass through the data.  This was the first function written for the package and the update mechanism was an easy way to write the function (and still works well).

The bigglm function came later and the models other than Gaussian require multiple passes through the data so instead of the update mechanism that biglm uses, bigglm requires the data argument to be a function that returns the next chunk of data and can restart to the beginning of the dataset.

Also note that the bigglm function usually only does a few passes through the data, usually this is good enough, but in some cases you may need to increase the number of passes.

Hope this helps, 

-- 

Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111