similar to: Bimodal deconvolution

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 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

Hello- My question is a short one. How can I specify a single point which through the fitted linear model has to go through? To illustrate my problem, the fit to following data must go through the point (-37.25(effect), 50(prob)). Note: you can ignore the label column. Effect Prob Label 1 -1143.75 7.142857 L 2 -572.75 21.428571 D 3 -223.75 35.714286 GL 4 123.25

Hi- I am trying to create a table of values by adding pairs of vectors, but am running into some problems. The problem is best expressed by a simple example. Starting with a data table "basis": atom x y z 1 Cu 0.0 0.0 0.0 2 Cu 0.5 0.5 0.5 I want to add 0.5 0.5 0.5 (and also the 0 0 0 but it wouldn't change the values below so I won't refer to it in the rest

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

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$parameters, what = "density")

Hi, I was looking for a package that would help with outlier detection for bimodal distributions. I have tried 'outliers' and 'extremevalues' packages, but am not sure if they are ok for bimodal distribution. Any help would be highly appreciated! thanks, [[alternative HTML version deleted]]

Hi, is there any package/function that can tell if a numeric vector (continuous data) has a bimodal or trimodal distribution and caluclate the location of the corresponding modes? Thanks

Hi All, I have a parameter that is bimodal, and I want to get some sort of linear model done with it results = some.linear.function(bimodal.param ~ factor1 + some other stuff, mydata) I want to see if factor 1 matters (it has 3 levels, of of which can be taken as baseline), i.e: summary(results) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.108522

Hi, Suppose I have the following vector (data points): > x [1] 36.0 57.3 73.3 92.0 300.4 80.9 19.8 31.4 85.8 44.9 24.6 48.0 [13] 28.0 38.3 85.2 103.6 154.4 128.5 38.3 72.4 122.7 123.1 41.8 21.7 [25] 143.6 120.2 46.6 29.2 44.8 25.0 57.3 96.4 29.4 62.9 66.4 30.0 [37] 24.1 14.8 56.6 102.4 117.5 90.4 37.2 79.6 27.8 17.1 26.6 16.3 [49] 41.4 48.9 24.1

We currently ship a PAE 32-bit domU that we can trivially make bimodal, except that if we set it to "bimodal", then older Xens will default to thinking the domU is not PAE: 353 dsi->pae_kernel = PAEKERN_no; 354 if ( dsi->__elfnote_section ) 355 { 356 p = xen_elfnote_string(dsi, XEN_ELFNOTE_PAE_MODE); 357 if ( p != NULL && strncmp(p,

Hello, Is there any test  for bimodality in R that x <- c(rnorm(1000,0,1),rnorm(1000,3,1)) hist(x,nclass=100) Thank you in advance for any help. Regards, Samor [[alternative HTML version deleted]]

I'm not sure where to begin with this, but I was wondering if someone could refer me to an R package that would test to see if a distribution fits a bimodal distribution better than a unimodal distribution. Thanks, Andrew [[alternative HTML version deleted]]

Hello R Users, I am doing a Latin Hypercube type simulation. I have found the improvedLHS function and have used it to generate a bunch of properly distributed uniform probabilities. Now I am using functions like qlnorm to transform that into the appropriately lognormal or triangularly distributed parameters for my modes. However I have a parameter which I believe is bimodally distributed,

Hi, Does anybody have a good tip of how to treat bimodal data to perform statistical analyses? My data set ranges from -1 to 1 (any values are posssible in between) and most data are either close to -1 or close to 1. They are the results of a two choice experiment where individuals could choose more than once in either direction and scores were calculated. Simone Simone Immler

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.

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

For several years, I have been using Splus to analyze an ongoing series of datasets that have a bimodal distribution. I have used the following functions, in particular the ms() function, to estimate the parameters: two means, two standard deviations, and one proportion. Here is the code I've been using in S: btmp.bi <- function(vec, p, m1, m2, sd1, sd2) {

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