similar to: When is *interactive* data visualization useful to use?

Hello Tal, You asked *When is it helpful to use interactive plots? Either for data exploration (for ourselves) and data presentation (for a "client")?* My answer: It's helpful for checking data quality, for exploration with and without "clients", for checking results, and for data presenting. Notes: (1) It's difficult to explain interactive data visualization in

I used the AR modelling written for R (S) on blood pressure and heart rate signals. I used 60 one second samples and a model order of 20. I used the "ar" finction in the "ts" package. Given that blood pressure is measured in mmHg would the spectral density (on the graph displayed be [mmHg]sq/Hz ? And the heart rate is measured in Beats Per Minute (bpm) - so would the

Greetings, I would like to estimate a spectral coherence between two timeseries. The stats : spectrum() returns a coh matrix which estimates coherence (squared). A basic test which from which i expect near-zero coherence: x = rnorm(500) y = rnorm(500) xts = ts(x, frequency = 10) yts = ts(y, frequency = 10) gxy = spectrum( cbind( xts, yts ) ) plot( gxy $ freq, gxy $

Dear list, I've recently become interested in comparing the spectral estimates using the different methods ("pgram" and "ar") in the spectrum() function in the stats package. With many thanks to the authors of these complicated functions, I would like to point out what looks to me like a bit of an inconsistency -- but I would not be surprised if there is good reasoning

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, 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, 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, just a general question: when we do hierarchical clustering, should we compute the dissimilarity matrix based on scaled dataset or non-scaled dataset? daisy() in cluster package allow standardizing the variables before calculating dissimilarity matrix; but dist() doesn't have that option at all. Appreciate if you can share your thoughts? Thanks John [[alternative HTML

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

Hello world, I am actually trying to transfer a lecture from Statistica to R and I ran into problems with spectral analysis, I think I just don't get it 8-( (The posting from "FFT, frequs, magnitudes, phases" from 2005 did not enlighten me) As a starter for the students I have a 10year data set of air temperature with daily values and I try to get a periodogram where the annual

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 <-

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

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 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 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 =

Hi everyone Can anyone tell me how I normalise a power spectral density (PSD) plot of a periodical time-series. At present I get the graphical output of spectrum VS frequency. What I want to acheive is period VS spectrum? Are these the same things but the x-axis scale needs transformed ? Any help would be greatly appreciated Tom

I have done a fair bit of spectral analysis, and hadn't finished collecting my thoughts for a reply, so hadn't replied yet. What exactly do you mean by normalize? I have not used the functons periodogram or spectrum, however from the description for periodogram it appears that it returns the spectral density, which is already normalized by frequency, so you don't have to worry about

Hi all, I am dealing with paleoceanographic data and I have a C14 time serie and one other variable. I would like to perform a spectral analysis (fft or wavelet) and plot it. Unfortunately I don't know the exact script to do this. Does anybody could send me an example to perform my spectral analysis ? I Thank you David Changez de tête et de tenue tous les jours si vous le voulez !

Hi there, I try to plot visible light spectrum (380nm~780nm) with color corresponding to the specific wavelength. However, I don't find a function that could do this. Another question, it's possible to plot a color space chromaticity diagram like this: http://upload.wikimedia.org/wikipedia/commons/thumb/0/02/CIExy1931.svg/300px-CIExy1931.svg.png Thanks in advance! Jinsong

For the command 'spectrum' I read: The spectrum here is defined with scaling 1/frequency(x), following S-PLUS. This makes the spectral density a density over the range (-frequency(x)/2, +frequency(x)/2], whereas a more common scaling is 2? and range (-0.5, 0.5] (e.g., Bloomfield) or 1 and range (-?, ?]. Forgive my ignorance but I am having a hard time interpreting this. Does this mean