Hi Thanks a lot,
I think you have covered the things I want to do for now so I will try to
implement them as soon I can.
<< A finite Fourier series could be the best tool IF the the multiple
periodicities are all integer fractions of a common scale.>>
This is certainly true for my repetitive "unit" (the smallest data peak) so
I hope this makes things easier.
Thanks
spencerg wrote:
>
> Dear Dr Gkikopoulos:
>
>
> 1. Have you looked at "bioconductor.org"? They have substantive
> extensions to R specifically for "genomic data", which I assume would
> include chromosome.
>
>
> 2. To "identify periodicities at different timescales", I agree
> with Stephen that "spectrum" would likely help.
>
>
> 3. The best software to "fit data into discrete number of curves"
> depends on the particular "discrete number of curves" you want to
> consider and how you want to "fit data into" them. A finite Fourier
> series could be the best tool IF the the multiple periodicities are all
> integer fractions of a common scale. In that case, using a "fourier"
> base in the "fda" package could be your method of choice. Otherwise,
> you might consider Bayesian Model Averaging. RSiteSearch("Bayesian
> Model Averaging") produced 80 hits for me just now, and
> RSiteSearch("Bayesian Model Averaging", "function") produced 60.
> "RSiteSearch.function" in the "RSiteSearch" package [available via
> install.packages("RSiteSearch",repos="http://r-forge.r-project.org";)]
> told me that 27 of the 60 were in the "ensembleBMA" package, and another
> 14 were in the "BMA" package.
>
>
> 4. The best way to "compare data from different experiments"
> depends on your evaluation of "3" above. The "fda" package includes an
> "fRegress" function that might be useful.
>
>
> Hope this helps.
> Spencer Graves
>
>
> trias wrote:
>> There are a couple of different goals for this projects
>>
>> *identify periodicities at different timescales (ie different dT)
>> *fit data into discrete number of curves, ie 6 different basic functions
>> should be enough to describe the basic repeating elements in this data
>> (ie 6
>> different categories of peaks)
>> *comapre data from different experiments of the same "time" reference
>> (in
>> my case this is location on chromosome) for changes in the underlying
>> basic
>> elements (ie changes of the basic funtions,periodicity etc)
>>
>> I think if I can find a strategy to answer some of these question I be
>> in a
>> good position to explore this data analysis further if needed.
>>
>> Thanks a lot
>>
>>
>>
>> stephen sefick wrote:
>>
>>> What is your end goal? If it is to try and account for the
>>> variability of the "timeseries" you may want to look at ?spectrum
>>> If it is to model the periodicity...
>>>
>>> Stephen Sefick
>>>
>>> On Fri, Apr 3, 2009 at 11:30 AM, trias
>>> wrote:
>>>
>>>> Here is the gif that didn't come through earlier
>>>> http://www.nabble.com/file/p22870832/signal.gif signal.gif
>>>> --
>>>> View this message in context:
>>>> http://www.nabble.com/Curve-fitting%2CFDA-for-biological-data-tp22868069p22870832.html
>>>> Sent from the R help mailing list archive at Nabble.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.
>>>>
>>>>
>>>
>>> --
>>> Stephen Sefick
>>>
>>> Let's not spend our time and resources thinking about things that are
>>> so little or so large that all they really do for us is puff us up and
>>> make us feel like gods. We are mammals, and have not exhausted the
>>> annoying little problems of being mammals.
>>>
>>> -K. Mullis
>>>
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