Sir/Ms
Is there a function within a bioconductor library that allows for the
generation of p-values or statistical significances?
Thanks
R Hartley
[[alternate HTML version deleted]]
Hi,
I think you need to be a bit more specific (and please do not repost
the same message). What hypothesis are you interested in? A p-value
is basically a piece of information that is relevant to a specific
hypothesis, since there are rather a large number of such hypotheses
and you have not said what you want it is a bit difficult to answer
you.
There are many, many different options for generating tests of
hypotheses and for doing lots of other things too.
Robert
On Wed, May 21, 2003 at 12:06:51PM +0100, Richard Hartley wrote:
> Sir/Ms
> Is there a function within a bioconductor library that allows for
the
> generation of p-values or statistical significances?
> Thanks
> R Hartley
>
> [[alternate HTML version deleted]]
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
--
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| Robert Gentleman phone : (617) 632-5250
|
| Associate Professor fax: (617) 632-2444
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| Department of Biostatistics office: M1B20
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| Harvard School of Public Health email: rgentlem@jimmy.harvard.edu
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I don't know if this is the best place to post this question but I
will
try anyway. I have two experiements for which I use one-way
matched-randomized ANOVA for the analysis and I would like to compare
different treatments in the two experiments. The only common group in
the two experiments are the controls. Is there any ANOVA design that
allows me to make this comparison taking into consideration the
confounding effect? Any help would be greatly appreciated.
Isaac
A representation of the experiments follows:
Experiment 1
Control1 Treat1 Treat2
Blk1 s1 s2 s3
Blk2 s4 s5 s6
Blk3 s7 s8 s9
Experiment 2
Control2 Treat3 Treat4
Blk1 s1a s2a s3a
Blk2 s4a s5a s6a
Blk3 s7a s8a s9a
Control1 and Control2 I are the same control cell line. I would like
to
compare Treat1 to Treat3 and Treat 4 and also I would like to compare
Treat2 to Treat3 and Treat4. The fact that those experiments are done
in
two different blocks will confound the interpretation. Can I use the
common control group to build a model? Should I include one of the
treatments in future experiments to test my model?
Any statistical test return a p-value, for example
look at the R-library "ctest" (Classical Tests) for
the most common ones.
Good job
A.S.
----------------------------
Alessandro Semeria
Models and Simulations Laboratory
The Environment Research Center - Montecatini (Edison Group),
Via Ciro Menotti 48,
48023 Marina di Ravenna (RA), Italy
Tel. +39 544 536811
Fax. +39 544 538663
E-mail: asemeria@cramont.it
