Hi Sean,
No, we do not provide a means for users to pass arguments to
maximizeInterpolant() and, I have no plans to permit this. My feeling is
that the iteration should provide a reasonable value within ten
iterations, or else there is something wrong and continuing the iteration
will not improve the situation. For example the requested accuracy of 8
significant figures might not be obtainable in machine precision, in which
case the final iteration is as good as can be achieved. Your data appears
to be underdispersed, so the estimation of tagwise dispersions is in a
sense degenerate. Since all the dispersions will be more or less 1e-8, it
doesn't seem very relevant whether each value iteration has converged to 8
significant places. This numerical accuracy might not be obtainable in
computer precision for such small dispersions.
Best wishes
Gordon
---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
sm...@wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth
On Fri, 22 Jul 2011, Sean Ruddy wrote:
Hi Gordon,
In addition to Kasper's email, is it possible to pass arguments to
maximizeInterpolant, for example to increase the max iterations? It appears
you can send additional arguments to dispCoxReidInerpolateTagwise but not to
maximizeInterpolant.
Thanks,
Sean
On Fri, Jul 22, 2011 at 6:39 AM, Kasper Daniel Hansen <
kasperdanielhan...@gmail.com> wrote:
On Fri, Jul 22, 2011 at 9:31 AM, Sean Ruddy <srudd...@gmail.com> wrote:
Hi Gordon,
Thanks for the reply. That's good to know I shouldn't be worried about
the results, but then again I'm not really sure, as you say, if I
should even go down this road. Using the standard edgeR normalization
would also be meaningless in my case unfortunately. I have two data
sets of counts each belonging to different organisms. One data set
runs smoothly and the other has all these problems yet the experiments
done on both were exactly the same. Kind of puzzling and unfortunate
but I will investigate more into what you're saying about the offsets
and see if I can't adjust my strategy. Thanks for the insights!
While I agree with Gordon that having warnings in a few genes are
hardly problematic, it would perhaps make sense to make it easy for
the user to identify exactly which genes (rows) have warnings
associated with them, Having been bitten by convergence problems in
the past, I believe it is always prudent to check the data. Just a
suggestion, and I apoligize if this is already (easily) possible.
Kasper
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