search for: deconvolut

I want to run deconvolution of a time series by an impulse or point-spread function through Wiener filter, regularized filter, Lucy-Richardson method, or any other approaches. I searched the CRAN website and the mailing list archive, but could not find any package for such a deconvolution analysis. Does anybody know an exi...

Hi Everyone- After searching through posts and my favorite R-help websites I'm still confused about a problem. I have data which is bimodal in nature, but there is no clearly obvious separation between the two peaks. In programs such as Origin, I can deconvolute the two distributions and have it generate a "best guess" as to what the two subpopulations are which make up my dataset. Below is a link to a figure which represents what I would like to accomplish here (it is the best I can do so far). http://nucleus.msae.wisc.edu/example.html Thank...

...spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks. Xiuli

...lex problem, but maybe it is a possibility to do it? First supposement is to use a nls() with very large functions, and compare AIC value, but it is very difficult to suggest any starting points for algotirithm. Searching google I have found only a description of commercial software for doing such deconvolution (Origin, PeakFit) without any information about used algorithms. No ready-to-use function in any language. I have tried to use a Mclust workaround for this problem, by generating a large dataset for which the spectrum is a histogram and feed it into the Mclust. The results seem to be serious, b...

...uot;*")+outer(c2,s2,"*")+outer(c2,s2,"*")) names(chrom.tot)[-1] <- x plot(s1~x,type="l",main="spectra") lines(s2~x,col=2) lines(s3~x,col=3) Thanks for your time Kind Regards Bart -- View this message in context: http://www.nabble.com/Chromatogram-deconvolution-and-peak-matching-tp22057592p22057592.html Sent from the R help mailing list archive at Nabble.com.

Dear R and sweave users A further problem, which I couldn't resolve, using the manual: In R I use the split.screen command to put e.g. two timecourses one above the other into one plot: split.screen(c(2,1)) screen(1) plot(stick,type='h', col="red",lwd=2) screen(2) plot(deconvolution.amplitude,type='h',col="blue",lwd=2) Is there a similar way, doing this in Sweave? many thanks Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

...v() [q,r] = deconv(v,u) deconvolves vector u out of vector v, using long division. The quotient is returned in vector q and the remainder in vector r such that v = conv(u,q)+r . If u and v are vectors of polynomial coefficients, convolving them is equivalent to multiplying the two polynomials, and deconvolution is polynomial division. The result of dividing v by u is quotient q and remainder r. I'll agree some help on the translation of this operator to R. Thanks in advance -- Ferran Carrascosa

Dear list members, I am using Mclust in order to deconvolute a distribution that I believe is a sum of two gaussians. First I can make a model: > my.data.model = Mclust(my.data, modelNames=c("E"), warn=T, G=1:3) But then, when I try to plot the result, I get the following error: > mclust1Dplot(my.data.model, parameters = my.data.model$par...

...ages: 1: Reached total allocation of # Mb: see help(memory.size) 2: Reached total allocation of # Mb: see help(memory.size) Execution halted -------------------------------------------------------- after attempting line: summary(EMclust(y),y) which is computationally intensive (performs a "deconvolution" of the data into a series of Gaussian peaks) and where # is either 766Mb or 2048Mb (depending on the max memory size I set). The call I make is to Rterm.exe (to try to avoid Windows overhead): "C:\Program Files\R\R-2.2.1\bin\Rterm.exe" --no-save --no-restore --vanilla --silent...

Hello list! I would like to fit a distribution to each of the peaks in a histogram, such as this: http://photos1.blogger.com/blogger/7029/2724/1600/DU145-Bax3-Bcl-xL.png . The peaks are identified using Petr Pikal peaks function ( http://finzi.psych.upenn.edu/R/Rhelp02a/archive/33097.html), but after that I am quite stuck. Any idea as to how I can: Fit a distribution to each peak Integrate the

On 08/05/2018 4:50 PM, Steven Nydick wrote: > It also does the same thing if the factor is not on the first level of > the list, which seems to be due to the fact that the islistfactor is > recursive, but if a list is a list-factor, the first level lists are > coerced into character strings. > > > x <- list(list(factor(LETTERS[1]))) > > unlist(x) > Error in