search for: amplitudes

Hi dear R community, I have up to 12 measures of a protein for each of 6 patients, taken every two or three days. The pattern of the protein looks periodic, but the height of the peaks is highly variable. It's something like this: patient <- data.frame( day = c(1, 3, 5, 8, 10, 12, 15, 17, 19, 22, 24, 26), protein = c(5, 3, 10, 7, 2, 8, 25, 12, 7, 20, 10, 5) ) plot(patient$day,

...gram too document, which they stated before delving into particularly how they normalized to the mean squared amplitude of the signal that the periodogram was computed from. In the page before, and perhaps this is where some of the confusion arises from, they document the calculations for MS and SS amplitudes and "time integral squared amplitude" of the signal in the "time" domain, not the frequency domain. The page after that, their example only shows how to normalize a periodogram so its sum is equal too the MS amplitude. In short, but starting from SS amplitude: a). sum(a[index=(...

Dear helpers I constructed a data frame with this structure > str(dados1) `data.frame': 485 obs. of 16 variables: $ Emissor : int 1 1 1 1 1 1 1 1 1 1 ... $ Marisca.Rio : int 1 1 1 1 1 1 1 1 1 1 ... $ Per?odo : int 1 1 1 1 1 1 1 1 1 1 ... $ Reproducao : int 3 3 3 3 3 3 3 3 3 3 ... $ Estacao : int 2 2 2 2 2 2 2 2 2 2 ... $ X30cm : int

...() which actually did equal the norming conventions that I've expected too see (MS or SS amplitude). I have tried various combinations of parameters in spec.pgram() to turn off tapering and spans, so that essentially there is no leakage correction of the data so I could reproduce the MS and SS amplitudes exactly, just as I did calculating them independently. Either that can not be done with spec.pgram(), or spec.pgram() is normalized too something other than the sum squared amplitude of the signal a[]. What does spec.pgram() normalize too? Perhaps the periodogram returned by spec.pgram() is norma...

Hi dear R community, I have up to 12 measures of a protein for each of 6 patients, taken every two or three days. The pattern of the protein looks periodic, but the height of the peaks is highly variable. I'm testing for periodicity using a Monte Carlo simulation envelope approach applied to a cumulative periodogram. Now I want to predict the location of the peaks in time. Of course, the

...daily oscillation when using the spectrum() function. What I am really interested in, is to find the amplitude corresponding to this oscillation. I work with a college using Matlab, and although we apply the same methodology, our results differs : we find the same frequencies, but not the same amplitudes. I put the code I use here to understand if the difference comes from the way that fft function are encoded int both software or from my own misunderstanding of the function. I am importing data as zoo, and verifying that they are regular spaced, and after, I simply use the non smoothed spectru...

When I call inbound with a cell phone (via SIP PSTN trunk) some of my prompts the first word is cut off. I'm assuming the prompt is needing to be transcoded on the fly and it's not getting transcoded fast enough. I did a file convert to create gsm versions (currently they are referenced in my dial plan with no extension Seem to have same problem. How do I determine which file

...ant solution omega2Day=omega2*Day m=lme(y~(cos(omegaDay)+sin(omegaDay)+cos(omega2Day)+sin(omega2Day))*treatment, random=~1|subject) summary(m) The summary table tells me that the respective elements are significant (except for cos(omegaDay):treatmentTRUE). I know how to calculate the amplitudes and phases for both treatment groups from the model estimates, but how do I know if they are significantly different? Thanks to anyone who takes the time to help me with this problem! Karin _________________________________________________________________ Hotmail: Vertraue...

Hello. I'm from Federal University of Rio Grande do Sul - Brazil and I'm trying to adapt speex_echo.h (speex-1.2beta3-win32) to our video-conference software code. We are working on amplitude values, but I realised that in order to AEC works, one has to convert amplitude to frequency and filter the echo frequencies. So, my question is: Do I have to manually convert amplitude samples to

Hi all,I had a question last week on asking for a function that will help me draw three different circles on x,y,z axis based on polar coordinates (Each X,Y,Z circle are coming from three independent measurements of 1-360 polar coordinates). It turned out that there ?is no such function in R and thus I am trying to write my own piece of code that hopefully I will be able to share. I have spent

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Hello, In doing a series of non-linear estimations of a function which is a sum of a varying number of sinusoids, I would like to "autogenerate" the arguments needed by nls() depending on that number. For example, when there are two sinusoids: > nls( y ~ mu + A1 * cos(2*pi*f1*x - P1) + A2 * cos(2*pi*f2*x - P2), data = some.xy.data, start = list( mu=some.value0,

Please look at what I see in your code below (run-on code mush) to understand part of why it is important for you to send your email as plain text as the Posting Guide indicates. You might find [1] helpful. [1] https://wiki.openstack.org/wiki/MailingListEtiquette -- Sent from my phone. Please excuse my brevity. On June 25, 2017 2:42:26 PM EDT, Alaios via R-help <r-help at r-project.org>

What I did was to plot your initial values, then plot the smoothed values and guess the constants. That is, I got an "eyeball" fit to the smoothed values. As I have described this as "gross cheating" in the past, you should either split your data, estimate on one subset and then test on another, or estimate on your data and test on a replication. If you get pretty much the same

>> Actually, computing the "power spectrum" for each frame of W shows >> how large an ammount of the original signal at time offset j the >> echo canceller thinks should be removed from the current input frame. > > Careful when looking at W because of how the real and imaginary parts > are packed in the array. Err. Ok, as I got it, 'bin 0' has it's

useR's, I want to create a "movie" of a sin function (from 0 to pi/2) using levelplot() in the lattice package. I basically want to create 20 or so plots of the sin function starting with an amplitude of 0 and ending at amplitude 1. By using a loop and plotting these in succession, it will have the appearance of a movie or animation. I believe I know how to do this part, but I am

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how

...sing libspeex in my application. What¡¯s wrong? .... If the input speech has an amplitude close to ¡¾2^15, it is possible that at decoding, the amplitude be a bit higher than that, causing clipping when saving as 16-bit PCM." This is one of FAQs in 'speex-manual' you wrote. I guess amplitudes close to 2^15 bother me. Do you have any idea to avoid it? I believe speex give me a solution or Plan B;) Thank you. >From: Dmitry Yakimov <support@zipcure.com> >To: ?? <dawn144kr@hotmail.com> >Subject: Re: [Speex-dev] a way restrict encoding sound volume >Date: Mon, 09...

Kindly send a cc to me when replying to the list. I'm having trouble using lmer beyond a first step. My data: > some(exp1B) sub ba amplitude a b c d 2 1 1.00 1.5 65 63 4 8 41 4 1.15 0.0 92 41 3 4 43 4 1.15 3.0 88 48 2 2 63 6 1.00 3.0 50 72 9 9 77 8 1.15 0.0 112 25 2 1 89 10 1.15 0.0 37 33 36 34 126

I have been attempting to estimate the periodic contribution of an effect to some data but have not been able to fit a sine wave within R. It would be nice to start by being able to fit a sine wave with an amplitude and frequency. x<-seq(0,20,by=0.5) y<-2*sin(2*pi*.5*x) #amplitude =2, frequency=0.5 # This failed to converge r<-nls(y ~ A*sin(2*pi*F*x), start=list(A = 1, F = 1),