similar to: Need some vectorizing help

In 2004 there was this R-Help posting from Jan 2004: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/33097.html R equivalent of Splus peaks() function? The peaks function there has worked well for me on a couple of projects, but some code using "peaks" failed today, which had worked fine in the past. I was looking for a peak in a test case that was a sine curve over one

Hello, Thank you to all those great folks that have helped me in the past (especially Dennis Murphy). I have a new challenge. I often generate time-series data sets that look like the one below, with a variable ("Phase") which has a series of flat-topped peaks (sample data below with 5 "peaks"). I would like to calculate the phase value for each peak. It would be great to

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

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

I'm sure there must be various peak-finding algorithms out there. Not knowing of any, I have written one myself*, but I thought I'd ask to see what's out there. Basically, I have a 2-dimensional data set and I want to identify local peaks in the data, while ignoring "trivial" peaks. My naive algorithm first identifies every peak and valley (point of inflection change

Hi All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background

Hello all, I have some histograms of amount of DNA in some cells (DU145 cells overexpressing Bax and Bcl-xL for those who wish to know). The histograms show not only two peaks as expected, but three, indicating that some cells have more than normal amounts of DNA. I am interested in knowing how much of the cell populations are in each peak as well as between. I am not really sure how to go

Is there any R package that can help me with digging out the maxima of a 1-D trajectory ? I have 975 1-D curves. They are only known as time series. That is a set of points ordered with respect to time. Some curves exhibit one only peak. Others have two peaks of different height. We wish to find the number of peaks and their position along the time axis. Apparently it's a trivial problem

Hello all I'm trying to do a simulation that involves identifying the minimum point between two peaks of a (usually) bimodal distribution. I can do this easily if there are only two peaks: CnBdens<-density(Ys/Xs) #probability density function for ratio of Ys to Xs for(p in 1:512) ifelse(CnBdens$y[p]>CnBdens$y[p-1],peak1<-p,break) #identifies first peak in probability

This is probably a trivial question, but I did not see it in the FAQs. Perhaps I simply need to be pointed to some URLs. In any case, I have some sampled data that has peaks, and I would like to extract peak height, location, and width, according to almost any statistical model. Gaussian would seem to make the most sense. There can also be "steps" increasing the amplitude over a

I've been asked in private, (and am replying BCC to the asker), >> I saw your post on the R-help archives page about the possibility of >> porting a function from S-Plus called peaks() to R. I am looking for >> some way to locate peaks in a simple x,y data set, and thought that R >> might be the way to go. "of course" it is the way to go, don't get

I have a seasonal time series. I want to calculate the annual mean value of the time series at its peak (say the mean of the three values before the peak, the peak, and the three values after the peak). The peak of the time series might change cycle slightly from year to year. # E.g., nPts <- 254 foo <- sin((2 * pi * 1/24) * 1:nPts) foo <- foo + rnorm(nPts, 0, 0.05) bar <- ts(foo,

Hi all. Sorry to post this here, but I figure that you may know the answer to my question... I analyze data sets very frequently, and it becomes cumbersome to try to find sustained peaks (my def - high points that exist for about 6 minutes or more - 3 poll intervals) in these data sets all the time. Right now I graph the data and then look for what looks to be a sustained peak and then go to

Hi all, I'm running an Icecast-2.2 server with at peak times some 50 sources and 500 concurrent listeners all using low-bitrate 16kpbs streams. I'm experiencing some connection losses at these peak times ("Client connection died" message in error.log). The machine running Icecast has a 100Mbit connection to the internet. It is a Celeron 2.4Ghz machine with 1Gbyte of main

Grrr ... new trial with code here: http://pastebin.com/RjHNNG9J Maybe the amount of inline-code prevented posting? Hello, I am writing a simple peak detector and it works quite well ... however there's one special case below, that I can't get my head wrapped around ... the problem is in the "Deal with not fully qualified peaks at the sequence extremes" section, but I cannot

Hello, I just notice this: > x <- c(1:4,0:5, 4, 11) > library(pastecs) Loading required package: boot > tp <- turnpoints(x) > extract(tp, no.tp = FALSE, peak = TRUE, pit = FALSE) [1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE Warning message: arguments after the first two are ignored in: UseMethod("extract", e, n, ...) >

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

This is a bit random but perhaps interesting: Attached is a histogram of code sizes for about 50,000 random C programs compiled by recent versions of llvm and gcc. x-axis is bytes in the text segment and y-axis is number of programs in each 100-byte bucket. Code size for a program is taken to be the smallest code size across -O0, -O1, -O2, -O3, and -Os, for a given compiler. I don't

Dear all, I wonder which R algorithm could perform a mountain clustering in an spatial grid, in other words, having the coordinates of a map (x,y ... or even more) and then the altitude/height at each point I would like to localise the peaks of that topological surface. Which R algorithm can perform this? I see it as a clustering problem where the peak should be at the center of each cluster.

ReplayGain is not much good for FM broadcasting, and debatable for streaming too. There is a limit on the peak volume that can be broadcast on an FM signal so it is best to have a song volume of 100% broadcast with 100% FM deviation. If an average song volume is very low, for example, but has some loud peaks in it, ReplayGain will increase the volume to the extent that the peaks will be way too