similar to: Hyperspec package: need to change spectra names in a stacked plot

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

Hi Folks... No code to troubleshoot here. I need some suggestions about the right terminology to use in further searching, and any suggestions about R pkgs that might be appropriate. I am in the planning stages of a project in which IR, NMR and other spectra (I'm a chemist) would be collected on various samples, and individual samples would be followed over time. The spectra will be feature

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.

Dear all, I'm looking for users of the hyperSpec package for handling (hyper)spectral or spectroscopic data in R which I maintain. First of all, I made a few announcements concerning the further development which can be found in the hyperSpec-help mailing list an on which I hope to get user feedback: see http://lists.r-forge.r-project.org/pipermail/hyperspec-help/2012-July/thread.html. My

Hi, I have a data frame Y with the following information, ORF spectra 1 YAL001C 2 2 YAL005C 21 3 YAL007C 2 4 YAL012W 8 5 YAL016W 24 6 YAL019W 3 7 YAL020C 2 8 YAL021C 7 9 YAL022C 3 10 YAL023C 6 11 YAL026C 2 12 YAL029C 1 13 YAL031C 1 14 YAL035W 48 15 YAL038W 173 16 YAL041W 4 17 YAL042W 4 18

Dear all, This is potentially a bug in spec.pgram, when the number of samples is odd,spec.pgramreturns a different result withfast = TRUE, the example below contains the two varieties with a reference spectrum calculated manually. the number of returned spectra is also larger (50 compared to 49) whenfast = TRUE x <- rnorm( 99 ) plot(spec.pgram(x, taper = 0 , detrend = FALSE , plot =

Dear R-Developers, could 'sample' gain a ... argument? As a convenience function, I added a sample Method to my hyperSpec class. This function however has a flag indicating whether the results should be returned directly as a hyperSpec object or rather as indices that give a random sample. For the moment, I use SetGeneric to add the dots argument, but this of course gives a warning

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 R users, I am a newbie. Just switched from MATLAB. So thanks a lot for your patience. I have 50000 spectra collected in field. Each spectra has two columns : Wavelength (56) and the actual measurement. Each measurement came in a different .txt file on disk (50000 files in total). I wrote a script that reads every spectra in a for loop and constructs two variables : Wavelength (56) and

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot

Hello, I'm still a newbie user and struggling to automate some analyses from SigmaPlot using R. R is a great help for me so far! But the following problem makes me go nuts. I have two spectra, both have to be fitted to reference data. Problem: the both spectra are connected in some way: the stoichiometry of coefficients "cytf.v"/"cytb.v" is 1/2. {{In the SigmaPlot

Hi all, I'll have to filter Raman spectra and I would like to do so in R. I'm trying to figure out how. Maybe one of you could help me ... This is what I have to do: General: separate the real spectrum (the set of peaks) from two contaminations: a drifting background and (white) noise Constraints (which should help the task): A - the spectrum is positive B - the spectrum is long

Hi I'm fairly new to R and am trying to analyse some large spectral datasets using stepwise regression (fairly standard in this area). I have a field sampled dataset, of which a proportion has been held back for validation. I gather than step() needs to be fed a regression model and lm() can produce a multiple regression. I had thought something like: spectra.lm <-

Dear R-helpers, I'm working on mass spectra in randomForest/R, and following the recommendations for the case of noisy variables, I don't want to use the default mtry (sqrt of nvariables), but I'm not sure up to which proportion mtry/nvariables it makes sense to increase mtry without "overtuning" RF. Let me tell my example: I have 106 spectra belonging to 4 classes, the

Dear collegues, I´ ve worked with near infrared (NIR) spectroscopy to assess chemical, physical, mechanical and anatomical properties of wood. I use "The Unscrambler" software to correlate the matrix of dependent variables (Y) with the matrix of spectral data (X) and I would like to migrate to R. The matrix of spectral variables is very large (2345 columns and n lines, where n =

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

Hi everyone, I am working on analyzing spectra which requires hundreds of kruskal wallis tests to be performed. Once significant results are found post tests are performed ect. My questions are: how do I perform all these kw tests and save just the pvalues in a table? Is there anyway to have R automatically perform post tests if a result of p<0.05 is found? Currently when I run the test

Dear all, I work with (vibrational) spectra: some kind of intensity (I) over frequency (nu), wavelength or the like. I want to do fourier transform for interpolation, smoothing, etc. My problem is that the spectra are often irregularly spaced in nu: the difference between 2 neighbouring nu varies across the spectrum, and data points may be missing. Searching for discrete fourier transform

hello all, for my diploma-thesis i want to statitically analyze near-infrared-spectra. a spectrum is given by the y-values of 1038 equi-distant x-points. in nature, a spectrum is a continuous curve. for analysis, every x-point is seen as a statistical variable. now my problem: first, i read a csv-table in a data.frame called sTable via read.table. besides some meta-data there are 1038 variables

Dear Group, I am looking for ways to use R and Matlab. Doing the data transformations in R and using the data in Matlab to analyze with some pre-defined scripts. Any good ways to transfer the data into matlab in its most recent version? I tried using R.matlab but the writeMat output is not readable by Matlab. I just need to output a data.frame and read it as is into matlab where I can do any