search for: pgram

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle...

hello, I have been using the per function in package longmemo to obtain a simple raw periodogram. I am considering to switch to the function spec.pgram since I want to be able to do tapering. To compare both I used spec.pgram with the options as suggested in the documentation of per {longmemo} to make them correspond. Now I have found on a variety of examples that there is a shift between the log of the periodogram with per and that with spec....

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE) "...

Can someone explain me this spec.pgram effect? Code: period.6<-c(0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10 ,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10) period.5<-c(0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10 ,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0...

Dear list, What on earth is spec.pgram() normalized too? If you would like to skip my proof as to why it's not normed too the mean squared or sum squared amplitude of the discrete function a[], feel free too skip the rest of the message. If it is, but you know why it's not exact in spec.pgram() when it should be, skip the rest o...

...at I am a newbie in R, so I apologize in advance if my questions seem to be too easy for you. Well, I'm looking for periodicity in histograms. I have histograms of certain phenomenons and I'm asking whether a periodicity exists in these data. So, I make a periodogram with the function spec.pgram. For instance, if I have a histogram h, I call spec.pgram by spec.pgram (h, log="no", taper=0.5). So, I have some peaks that appear and I would like to interpret them but I do not know how they are computed and so what a peak with a value of 10000 represents in comparison with a peak of v...

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.pgr...

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 that justifies it that I a...

Dear R users I have a really simple question (hoping for a really simple answer :-): Having estimated the spectral density of a time series "x" (heart rate data) with: x.pgram <- spectrum(x,method="pgram") I would like to compute the power in a specific energy band. Assuming that frequency(x)=4 (Hz), and that I am interested in the band between f1 and f2, is the power in the band simply the following? sum(x.pgram$spec[(x.pgram$freq > f1/frequency(x)...

...ncies. 5. One way of choosing m is mentioned in : https://web.archive.org/web/20080221221221id_/http://www.stat.uiuc.edu/~ombao/PAPERS.dir/gcvbmka.pdf I am looking for an R package which implements 5. Here is a reprex: # 1. Periodogram which may be biased plot(spectrum(lh,taper= 0, method="pgram"),log="dB") # 2. Using the default in built cosine taper plot(spectrum(lh,taper = .3, method="pgram"),log="dB") # 2. Again, using slepian taper library(multitaper) # I choose: n = length(lh), k =1, nw=2 mytaper = dpss(n=length(lh), k=1 , nw=2, returnEigenvalues=...

Hello! I tried to contact author of the package, but I got no reply. That is why I write it here. This might be useful for those who were using cts for spectral analysis of non-uniformly spaced data. In file spec.ls.R from cts_1.0-1.tar.gz lines 59-60 are written as pgram[k, i, j] <- 0.5 * ((sum(x[1:length(ti)]* cos(2 * pi * freq.temp[k] * (ti - tao))))^2/sum((cos(2 * pi * freq.temp[k] * (ti - tao)))^2) + (sum(x[1:length(ti)] * sin(2 * pi * freq.temp[k] * (ti - tao))))^2 ===> ) <=== /sum((sin(2 * pi * freq.temp[k] * (ti - tao)))^2) Is there a misplaced b...

Hopefully this will not seem too ignorant of a question. I am having a hard time picking out the sources of the differences between: abs(fft(x))^2/length(x) and spec.pgram(x, taper=0, log="no", plot=FALSE) Also from the limited testing that I have done since the DC "frequency" is not returned from spec.pgram how can I tell what has happened to the series when I specify demean = TRUE or detrend = TRUE? Any help? Thank you. Kevin

Hello! I wonder if there is a typo in detrending code of spec.pgram in spectrum.R from stats package. One can see in the code https://svn.r-project.org/R/trunk/src/library/stats/R/spectrum.R . I am afraid there is a typo and the code should look like if (detrend) { t <- 1L:N - (N + 1)/2 sumt2 <- N * (N^2 - 1)/12 for (i in 1L:ncol(x))...

Dear R-Help, I wish to simulate a process so that it has certain properties in the frequency domain. What I attempted was to generate a random time-series signal, use spec-pgram(), apply a function in the frequency domain, and then inverse transform back to the time-domain. This idea does not seem as straight forward in practice as I anticipated. e.g. x<-ts(rnorm(1000, 0,1), frequency=256) plot(x) ## looks like noise. pgm<-spec.pgram(x, taper=0) ## a flat spectra...

Any Ideas to get an interactive periodogram? -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis

Dear useRs, I'd like to plot a confidence interval on a periodogram. My problem is that spec.pgram(sunspots,ci=0.95,log="yes") gives me a blue error bar on the plot, but spec.pgram(sunspots,ci=0.95,log="no") does not. My questions are: 1. how should I plot the confidence interval with log="no"? 2. how should I get the min and max values of the confidence interval?...

...rst). I would like to know what x is. I think I am looking for the phase shift? The coherency? Cross-spectrum? And I have read the help files, Shumway and Stoffer has a small section on this, I don't have journal access, and I am having trouble finding anything on the internet. s = spec.pgram(x) par(mfrow = c(2,1)) plot(s, plot.type = "coh") plot(s, plot.type = "pha") Can R do the cross-spectrum of two timeseries? How? -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up a...

...ons besides laziness or carelessness that people will consistently error in language use, such as learning disorders, head injuries, and/or vertigo. On the contrary, I am aware of the definition of a periodogram, and I know what the unnormalized periodogram in the data I presented looks like. Spec.pgram() is actually normalized too something, because it's discrete integral is not well above the SS amplitude of the signal it computed the periodogram for. In other words, the powers are not in units of around 4,000, which the peak would be if the units were merely the modulus squared of the Fouri...

...el that I fit to EEG data. The autocorrelation plot of the residuals shows the series is now approximately white (i.e. ACF = 1 at lag 0, and close to 0 for all other lags). I would like to show that the spectrum of the series is also approximately white. When I calculate the periodogram using spec.pgram, what I get indeed looks white, but I would like to add horizontal threshold lines between which one can be 95% confident the spectrum is white. Thanks, -Andre [[alternative HTML version deleted]]

Dear list, I had some confusion regarding what function too use in order too relate results from spec.pgram() too a chi-square distribution. The documentation indicates that the PSD estimate can be approximated by a chi-square distribution with 2 degrees of freedom, but I am having trouble figuring out how to do it in R, and figuring out what specifically that statement in the documentation means. I have...