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,
Niklaus Fankhauser niklaus.fankhauser at 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
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 TimeSeries
On Tue, Dec 13, 2011 at 10:53 AM, Hans W Borchers
hwborch...@googlemail.com wrote:
Niklaus Fankhauser niklaus.fankhauser at 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
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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