[BioC] memory problem with fitPLM in package affyPLM

fhong at salk.edu fhong at salk.edu
Thu Jun 9 20:48:18 CEST 2005


>> > There were significant changes in the structure of the PLMset object
>> > between 1.2.x and 1.3.x which is why you are having problems with the
>> > boxplot(), Mbox() commands on your old PLMset using the new code.
>> But why when I reload in to R 2.0.1 ( the on ei used to generate PLMset
>> object), and tried boxplot ( suppose to produce NUSE plot), it gave me
>> something strange (see attachment)
> try something like
> boxplot(Pset,ylim=c(0.9, 1.2))
Does this mean for version 1.3.3, you set up a ylim for both boxplots (RLE
and NUSE). The new RLE and NUSE plots I got from version 1.3.3 look
different from the ones from version 1.2.5, the extremem outliers didn't
show up in the former.

> though I am not really too sure why you have such extreme outliers on
> your plot.
Very strange. Maybe due to a very bad chip ?

>> > Also, if you can live without the weights (or alternatively the
>> > residuals) you could do
>> >
>> > Pset <- fitPLM(my.Data,output.param=list(varcov="none",weights=FALSE))
>> >
>> >
>> > or
>> >
>> > Pset <-
>> fitPLM(my.Data,output.param=list(varcov="none",residuals=FALSE))
>> >
>> > which would also reduce the memory overhead.
>> Will those simplificaiton change the underlying model that is fitted to
>> the data. e.g., weights=FALSE doesn't this mean it won't use iteratively
>> reweighted least squares (IRLS)?
> No the fitting procedure will be unchanged, ie it still uses IRLS. All
> it means is that the weights aren't kept around after they have been
> used. Otherwise given that there is a weight for every PM probe a lot of
> memory gets used up.
Make sense, thanks!



Fangxin Hong  Ph.D.
Plant Biology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
E-mail: fhong at salk.edu
(Phone): 858-453-4100 ext 1105

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