Dear Thomas and John,
Thanks for your prompt reply and for looking at your code to explain
these differences. I see you replied very late at night, so I am sorry
if my little problem kept you awake!
As you pointed out, the model indeed converges (as reported in
model$converged) once I specify a larger number of iterations.
A very minor comment: it seems that the reporting of the estimates in
summary.biglm() is not taking the parameters from options(digits). For
example, using the same data and models as before:
> require(biglm)
> options(digits=8)
> dat=data.frame(y =c(rpois(50000, 10),rpois(50000, 15)),
ttment=gl(2,50000))
> m1 <- glm(y~ttment, data=dat, family=poisson(link="log"))
> m1big <- bigglm(y~ttment , data=dat, family=poisson(link="log"),
maxit=20)
> summary(m1)
<Snipped>
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.3019509 0.0014147 1627.21 < 2.2e-16 ***
ttment2 0.4052002 0.0018264 221.86 < 2.2e-16 ***
> summary(m1big)
Coef (95% CI) SE p
(Intercept) 2.302 2.299 2.305 0.001 0
ttment2 0.405 0.402 0.409 0.002 0
To get more digits I can extract the point estimates using coef(m1big),
but after looking at str(m1big) the only way I could figure to extract
the p-values was:
> summary(m1big)$mat[,"p"]
(Intercept) ttment2
0 0
Is there a way I can get summary.biglm() to report more digits directly?
Thanks again,
Francisco
--
Francisco J. Zagmutt
Vose Consulting
2891 20th Street
Boulder, CO, 80304
USA
www.voseconsulting.com
Thomas Lumley wrote:
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-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
Behalf Of Francisco J. Zagmutt
Sent: March-16-09 10:26 PM
To: r-h...@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@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@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
tlum...@u.washington.edu University of Washington, Seattle
______________________________________________
R-help@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@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.
