Hi!
thanks a lot for this suggestion! I tried to implement it like this, and
it worked nicely.
I used the method suggested by Gabor Grothendieck for simplification:
frml <- gene_expression ~ sin(tpoints * afreq + phase) * amp + shift
gridfit <- nls2(frml, algorithm = "grid-search", data=gendat, s
On Tue, Dec 13, 2011 at 10:53 AM, Hans W Borchers
wrote:
> Niklaus Fankhauser cell.biol.ethz.ch> writes:
>
>> I'm using nls to fit periodic gene-expression data to sine waves. I need
>> to set the upper and lower boundaries, because I do not want any
>> negative phase and amplitude solutions. Thi
Niklaus:
1. First, you mat not need to use nls at all, although I am not
familiar with the "port" algorithm, so I could very well be wrong
about this. Generally speaking, one uses time series methods (e.g.
fourier analysis) to fit periodic sine waves, so you may wish to check
out CRAN's TimeSerie
Niklaus Fankhauser cell.biol.ethz.ch> writes:
> I'm using nls to fit periodic gene-expression data to sine waves. I need
> to set the upper and lower boundaries, because I do not want any
> negative phase and amplitude solutions. This means that I have to use
> the "port" algorithm. The problem i
I'm using nls to fit periodic gene-expression data to sine waves. I need
to set the upper and lower boundaries, because I do not want any
negative phase and amplitude solutions. This means that I have to use
the "port" algorithm. The problem is, that depending on what start value
I choose for phase
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