Full_Name: Bonnie LaFleur Version: 1.6.1 OS: Windows Submission from: (NULL) (160.129.25.106)
All,
I sincerely apologize for being so vague with my "bugs" query. Part of this is that it is not directly related to a specific data analysis, but rather a part of a bigger project I am working on. I also apologize if the bugs forum is not appropriate for this problem. Thank you Prof Brian Ripley for your point to the glm.control() command, I will work with this for now and it will solve my most immediate problems.
Specifically, I am running about 10,000 + permutations through an R program
on a bewoulf cluster of machines, and needed to get a handle on whether the
lines and columns of my report output file are consistant, so I can use this
in debugging the overall program for the parallel processing, as well as
to keep track of any permutations for which a nonlinear program may not
converge.
In trying to answer the number of columns question I tried to find a problem
which I pretty much know has no reason to converge. It is totally nonsensical
data (as you
can tell). It does not converge in SAS or Splus for Windows, it does however
converge in R (version 1.6.1) for Windows and R (version 1.3.1) on linux -
though, of course the stimates are obviously suspect. I am enclosing simple R
commands
for these silly data for your perusal. Thank you for you time, and again,
I am sorry for the premature post last night.
Bonnie
R : Version 1.3.1 (2001-08-31) (on linux)
Y <- c(1,1,1,1,0) X1 <- factor(c(0,0,0,1,1)) X2 <- factor(c(0,0,1,0,0))
logist<- glm(Y ~ X1*X2, family=binomial(link="logit")) summary(logist) ## usual logistic output logist$converged ## TRUE
Well, others can speak for themselves, but R does for this data exactly what I would want a generalized linear model program to do. R finds the correct fitted values c(1,1,1,0.5,0,5) to 5 decimal places and the correct residual deviance -4*log(0.5) to 4 decimal places. The fitted values for the coefficients and theoreticaly infinite, but R does the best that can be done by giving large finite values and small t-statistics.
It is true that the fitted coefficients cannot converge for these data, because the stationary values are at infinity, but the fitted values and residual deviance can and do converge.
Gordon
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