similar to: Deconvolution of a spectrum

Dear useRs, I need to compute zero of polynomial function fitted by lm. For example if I fit cubic equation by fit=lm(y~x+I(x^2)+i(x^3)) I can do it simply by polyroot(fit$coefficients). But, if I fit polynomial of higher order and optimize it by stepAIC, I get of course some coefficients removed. Then, if i have model y ~ I(x^2) + I(x^4) i cannot call polyroot in such way, because there is

Dear useRs, Is there any more elegant way to convert dataframe to a vector of all its values than as.vector(as.matrix(x)) ? I did not have to do such conversion yet, so I am not sure... (of course as.vector() alone does not work). Regards, -- Lukasz Komsta Department of Medicinal Chemistry Medical University of Lublin Jaczewskiego 4, 20-090 Lublin, Poland Fax +48 81 7425165

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, I'm using a fairly simple HP Compaq desktop PC running Windows 2K. When running a large process in R, the process "RGUI.exe" will never exceed 50% of the CPU usage. The program used to be able to use more of the computer, but does not now. I don't believe this is a multiple processor machine. Can anyone give any advice on how to solve the problem? Thanks, Michael

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

Hi, I'm trying to match peaks between chromatographic runs. I'm able to match peaks when they are chromatographed with the same method, but not when there are different methods are used and spectra comes in to play. While searching I found the ALS package which should be usefull for my application, but I couldn't figure it out. I made some dummy chroms with R, which mimic my actual

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

Dear All, I have small samples of data (between 6 and 15) for numerious time series points. I am assuming the data for each time point is normally distributed. The problem is that the data arrvies sporadically and I would like to detect the number of outliers after I have six data points for any time period. Essentially, I would like to detect the number of outliers when I have 6 data points then

Hello, Has anyone written R functions for applying self-controlled case series methods (http://statistics.open.ac.uk/sccs/). In fact only thing needed is to modify glm function to allow absorption of effect. Eg. in Poisson model individual effect is used as factor, but it is considered as nuisance term where parameter estimates are not needed. Could anyone point how absorbing individual

Hi, I know that the type of model described in the subject line violates the principle of marginality and it is rare in practice, but there may be some circumstances where it has sense. Let's take this imaginary example (not homework, just a silly made-up case for illustrating the rare situation): I'm measuring the energy absorption of sports footwear in jumping. I have three models (S1,

On Jan 9, 2020, at 4:39 PM, Gene Heskett <gheskett at shentel.net> wrote: > > So for starters, what's the best ./configure command line? There’s this page for matching the layout of an existing Debian install: https://github.com/networkupstools/nut/wiki/Building-NUT-on-Debian,-Raspbian-and-Ubuntu But remind me, what are you hoping to get from the later version of NUT? (We don’t

Dear useRs, I would like to summarize results of several tests in groups of two columns - statistic, p-value, statistic, p-value etc. There would be nice to add significance stars, but printCoefmat allows to do it only to last column. Is there any way to do format such table without writing my own complicated function? Thank you in advance, -- Lukasz Komsta Department of Medicinal

Dear R users I have two questions about estimating the spectral power of a time series: 1) I came across a funny thing with the following code: data(co2) par(mfrow=c(2,1)) co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3) co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3) The first plot displays the frequencies ranging from 0 to 6 whearas the second plot displays the same curve but

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

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 Jan 9, 2020, at 5:51 PM, Gene Heskett wrote: > > On Thursday 09 January 2020 16:59:12 Charles Lepple wrote: > >> On Jan 9, 2020, at 4:39 PM, Gene Heskett <gheskett at shentel.net> wrote: >>> So for starters, what's the best ./configure command line? >> >> There’s this page for matching the layout of an existing Debian >> install: >>

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

Hi, I need some help using the R outliers package. I would like to perform a Q-test (Dixon test) on my data set. I used the dixon.test function, but I cannot understand what is the confidence level used to perform the test. I have n=101 (n= number of data). So, can I use directly dixon.test ? What about qdixon and qtable functions? thank you so much! -- View this message in context:

A record company is releasing next month what it calls copy-protected CDs which apparently cannot be ripped by CD readers. Major labels are waiting to see how it goes. http://news.cnet.com/news/0-1005-200-4099854.html --- >8 ---- List archives: http://www.xiph.org/archives/ Ogg project homepage: http://www.xiph.org/ogg/ To unsubscribe from this list, send a message to

Hi, I'm wondering why the spectrum() phase of quadrature couple isn't purely +/-pi. But mostly, I'm looking for a recommended way to take a 2-D spectrum and convert it into a single complex array. Kindly consider: # 10 Hz sine wave 10 seconds long sampled at 50 Hz deltaT = 1/50 t = seq(0, 10, deltaT) w = 2 * pi * 10 x = ts( sin( w * t ), deltat = deltaT ) y = ts( sin(