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

[Thomas Lumley]

Yes, the slow convergence is easier to get with the log link.  Overshooting the correct coefficient vector has more dramatic effects on the fitted values and weights, and in this example the starting value of (0,0) is a long way from the truth.   The same sort of thing happens in the Cox model, where there are real data sets that will cause numeric overflow in a careless implementation.

It might be worth trying to guess better starting values: saving an iteration or two would be useful with large data sets.

      -thomas


On Tue, 17 Mar 2009, John Fox wrote:

> Dear Francisco,
>
> I was able to duplicate the problem that you reported, and in addition
> discovered that the problem seems to be peculiar to the poisson family.
> lm(y~ttment, data=dat) and biglm(y~ttment, data=dat) produce identical
> results, as do glm(y~ttment, data=dat) and bigglm(y~ttment, data=dat).
> Another example, with the binomial family:
>
>> set.seed(12345)
>> dat=data.frame(y =c(rpois(50000, 10),rpois(50000, 15)),
> ttment=gl(2,50000))
>> dat$y0 <- ifelse(dat$y > 12, 1, 0)
>> m1b <- glm(y0~ttment, data=dat, family=binomial)
>> m1bigb <- bigglm(y0~ttment , data=dat, family=binomial())
>> coef(m1b)
> (Intercept)     ttment2
>   -1.33508     2.34263
>> coef(m1bigb)
> (Intercept)     ttment2
>   -1.33508     2.34263
>> deviance(m1b)
> [1] 109244
>> deviance(m1bigb)
> [1] 109244
>
> I'm copying this message to Thomas Lumley, who's the author and maintainer
> of the biglm package.
>
> Regards,
> John
>
>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On
>> Behalf Of Francisco J. Zagmutt
>> Sent: March-16-09 10:26 PM
>> To: r-help at stat.math.ethz.ch
>> Subject: [R] bigglm() results different from glm()
>>
>> Dear all,
>>
>> I am using the bigglm package to fit a few GLM's to a large dataset (3
>> million rows, 6 columns).  While trying to fit a Poisson GLM I noticed
>> that the coefficient estimates were very different from what I obtained
>> when estimating the model on a smaller dataset using glm(), I wrote a
>> very basic toy example to compare the results of bigglm() against a
>> glm() call.  Consider the following code:
>>
>>
>> > require(biglm)
>> > options(digits=6, scipen=3, contrasts = c("contr.treatment",
>> "contr.poly"))
>> > dat=data.frame(y =c(rpois(50000, 10),rpois(50000, 15)),
>> ttment=gl(2,50000))
>> > m1 <- glm(y~ttment, data=dat, family=poisson(link="log"))
>> > m1big <- bigglm(y~ttment , data=dat, family=poisson(link="log"))
>> > summary(m1)
>>
>> <snipped output for this email>
>> Coefficients:
>>              Estimate Std. Error z value Pr(>|z|)
>> (Intercept)  2.30305    0.00141    1629   <2e-16 ***
>> ttment2      0.40429    0.00183     221   <2e-16 ***
>>
>>      Null deviance: 151889  on 99999  degrees of freedom
>> Residual deviance: 101848  on 99998  degrees of freedom
>> AIC: 533152
>>
>> > summary(m1big)
>> Large data regression model: bigglm(y ~ ttment, data = dat, family =
>> poisson(link = "log"))
>> Sample size =  100000
>>               Coef  (95%   CI)    SE p
>> (Intercept) 2.651 2.650 2.653 0.001 0
>> ttment2     4.346 4.344 4.348 0.001 0
>>
>> > m1big$deviance
>> [1] 287158986
>>
>>
>> Notice that the coefficients and deviance are quite different in the
>> model estimated using bigglm(). If I change the chunk to
>> seq(1000,10000,1000) the estimates remain the same.
>>
>> Can someone help me understand what is causing these differences?
>>
>> Here is my version info:
>>
>> > version
>>                 _
>> platform       i386-pc-mingw32
>> arch           i386
>> os             mingw32
>> system         i386, mingw32
>> status
>> major          2
>> minor          8.1
>> year           2008
>> month          12
>> day            22
>> svn rev        47281
>> language       R
>> version.string R version 2.8.1 (2008-12-22)
>>
>>
>> Many thanks in advance for your help,
>>
>> Francisco
>>
>> --
>> Francisco J. Zagmutt
>> Vose Consulting
>> 2891 20th Street
>> Boulder, CO, 80304
>> USA
>> www.voseconsulting.com
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle