similar to: Chromatogram deconvolution and peak matching

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 existing R function for deconvolution? TIA, Gang

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

Dear useRs, I have a curve which is a mixture of Gaussian curves (for example UV emission or absorption spectrum). Do you have any suggestions how to implement searching for optimal set of Gaussian peaks to fit the curve? I know that it is very complex 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

I have two chromatograms I want plotted on the same axes. I would like the plots to be transparent, so the first chart is not obscured. I have tried adjustcolor(..., alpha.f=0.3), the problem is that my chromatogram is so dense with datapoints that they overlap and the entire graph just ends up a solid color. The second histogram still obscures the first. Consider this example: col1 <-

Hello! I am having an issue with the OrgMassSpecR package. I run my HPLC using a DAD detector. My raw data is exported form chemstation as a csv file. I then upload the csv into Rstudio no problem. Using the DrawChromatogram function, I get a nice chromatogram, and my retention time, peak area, and apex intensity values are given as well. The problem comes with the peak area value given. The

http://r.789695.n4.nabble.com/file/n3312061/x_and_y_values.txt x_and_y_values.txt I have the absorbance values form HPLC Chromatogram. I need to find the peaks in datapoints and area under each peak. I am not sure how how to find the peaks, I tried couple of libraries and peak function but they return me a list of values which when computed from area turns out to be huge nnumbers. In the

Hello all, I'm trying to calculate the standar desviation and I'm using the function sd(x,na.rm=TRUE) and I have this error:? Error in var(x, na.rm = na.rm) : no complete element pairs . Why happen this?, What can I do to solve it?. x is list of three numbers which I have from a table. Thanks so much from Spain Carlos Morales Diego

Hello all, I've run into what I bet is a silly problem; however, I've been trying to get around it now for a couple weeks and every time I think I have the answer it still doesn't work. So I apologize in advance if this is painfully obvious, but I've run out of ideas and would really appreciate any input. My situation is this, I'm importing a number of tab

I would really appreciate it if someone can give suggestions on how to do 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.

Hi all, I wrote a r program as below: x <- 1:10 y <- c(3,3,3,3,3,3,3,3,3,3) fit <- lm(log(y) ~ x) summary(fit) And I expect to get some error message from R, because "y" is constant. But, I got the message as below: > summary(fit) Call: lm(formula = log(y) ~ x) Residuals: Min 1Q Median 3Q Max -6.802e-17 -3.933e-17 -1.063e-17 1.807e-17

Hallo! <?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> <o:p> </o:p> I want to compare different samples with the formula … <o:p></o:p> <o:p> </o:p> Here some example data: <o:p></o:p> <o:p> </o:p> <o:p> </o:p> sample1<o:p></o:p>

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

I'm translating a matlab routine to R and I need some equivalent to deconv(): Description: deconv() [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

Interesting problem. I would discretize the x-values and interleave them. Lines from one dataset still overlap, so you see high- density and low-density regions, but lines from the other dataset are drawn into the interval. Like so: interleave <- function(x, MIN, MAX, N, nChannel = 2, channel) { isp <- seq(MIN, MAX, length.out = N + 1) # interleave support points offset <-

I have 120 MS chromatograms containing two columns'Time' and 'Peak height'. how do in create a msSet file which is required by all the functions of 'msProcess' to process these files. I would appreciate help very much. Thanks Sharad -- View this message in context: http://r.789695.n4.nabble.com/msProcess-creating-msSet-file-s-tp3727959p3727959.html Sent from the R help

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

Dear Developers: I have a question about memory management in R 2.2.1 and am wondering if you would be kind enough to help me understand what is going on. (It has been a few years since I have done software development on Windows, so I apologize in advance if these are easy questions.) ------------- MY SYSTEM ------------- I am currently using R (version 2.2.1) on a PC running Windows 2000

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

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

Greetings, I am new to R, but trying to put in the time to learn. I have read the R manual and several other introductory texts; however, there is nothing like actually putting it into practice. So here is my problem, and its more of a learning exercise for myself than anything else, but I'm stuck and getting extremely frustrated that I can't figure it out. I'm trying to make a