search for: extremas

Hi folks, I'm attempting to use the EMD package to analyze some neuroimaging data (timeseries with 64 channels sampled across 1 million time points within each of 20 people). I found that processing a single channel of data using EMD::emd() took about 8 hours. Exploration using Rprof() suggested that most of the compute time was spent in EMD::extrema(). Looking at the code for EMD:extrema(),

I have a vector with about 100.000 values representing a quite regular function (sinusoid like). I would like to find all local maxima of this function (should be about 4000). Is there a native routine for R? Thanks in advance Eryk. -- _|_ \|/ \|/ Eryk Witold Wolski tel :0049-(0)30-8413-1543 w w ?v? 'v? \'v'/ MPI Moleculare Genetik fax :0049-(0)30-8413-1139 |

I cannot understand the following error printed out when I try to get the extrema of my time series. I would appreciate some suggestion as I really cannot interpret the error. I might not be using a proper set of parameters in calling such functions. I am learning by doing ... > aa.peak <- wavCWTPeaks (aa.tree) Error in `row.names<-.data.frame`(`*tmp*`, value = c("1",

¿Alguien sabe si el test de reacciones extremas de Moses está escrito en algún paquete de R? Gracias de antemano.

...nal means,...). I would like to create a color palette which works for each map, so I need a color palette between -40 and +40?C. Sometimes my data for one map range from -10 to +20, sometimes from 10 to 30, etc... but always between -40 and +40?C. I would like a fluent color gradation between my extremas (-40 and +40), with different colors between customed values. For example, if the temperature is under -20?C I would like the color "darkblue", then if the temperature is between -20 and 0?C I would like the color "lightblue", then between 0 and 20?C the color "yellow&quot...

...is a periodic function to be fitted to a non linear model, but where no breakpoints should occur. So basically, the breakpoints would only happen in the (a,b) pair of coefficients, which would be a linear regression. However, as f is unknown, this makes things harder and I don't have a lot of extremas (min/max) to detect breakpoints robustly... 2) To detect breakpoint with an harmonic model and then to apply my non linear regression on each segment. These two ideas could potentially work, however these are workarounds. Thank you for your advices ! -- View this message in context: http://r....

Hi all, as I am trying to move slowly from just "working" to "good" code, I'd like to ask if there's a smarter way than using a for-loop in tasks like the example below. I need to obtain the extrema of the cumulated sum of a detrended time series. The following code is currently used, please have a look at the comments for my questions and remarks: system.time({ X

Full_Name: Karsten Krug Version: 2.4.0 OS: Open Suse 10.0, Windows XP Submission from: (NULL) (88.134.13.50) I found a problem in the 'optimise' function for one dimensional optimisation. Example 1: Try to find a maximum of the function below with the use of 'optimise' in the interval [0,0.5]. The function follows a parabola and has two local maxima located at the margins of

> Dear R Helpers! > > bgtest{lmtest} performs the Breusch-Godfrey test for higher > order serial > correlation. > > Is the Higher Order Correlation function already programmed in > R I couldn't find it? > > Sergei Petrov > ?acf ?pacf HTH, Bernhard ---------------------------------------------------------------------- If you have received this e-mail

I have attached the signal that causes the error message in this email subject. Only columns 1 and 3 have to be considered. It is the work trajectory of a molecule migrating between two equilibrium conformations. The curve has 2 peaks, as shown in its plot. But I keep missing the 2nd one. Here is my short script: library(wmtsa) setwd("C:/Documents and Settings/Monville/Alanine

Here is another occurrence of wmTSA internal error. My time series is a short breathing cycle (2425-Cyle_9.txt). Since wmtsa functions that extract extrema seem to expect longer series than I have, I tried the folowing two tricks: 1) I prolong the 1-cycle series on both ends through duplicating the first value (on the left end( and the last value on the right end as any ties as the 1-cycle

In my previous experience with wmtsa function wavCWTPeaks I realized the Tolerance vector passed to the multi-scale decomposition function "wavCWTTree" is crucial for detecting the time series extrema and prevent the R script from aborting. By trial & error I recently managed to figure out the Tolerance vector values in a single case (please see attachments). I am looking for some

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Hello The excellent adapt package integrates over multi-dimensional hypercubes. I want to integrate over a multidimensional simplex. Has anyone implemented such a thing in R? I can transform an n-simplex to a hyperrectangle but the Jacobian is a rapidly-varying (and very lopsided) function and this is making adapt() slow. [ A \dfn{simplex} is an n-dimensional analogue of a triangle or

A few general comments about stepwiseAIC and a suggestion of how to select models a) Apart form the problem, that stepwise selection is not a garanty to get the best model, you need to have a lot of data to avoid overfitting if your model includes 7 parameter plus interactions (> 10 observations per parameter is what you are ideally looking for). b) Have a look at Anderson and Burnham's

Hi, Short Version: Is there a function to re-bin a histogram to new, broader bins? Long version: I'm trying to create a histogram, however my input-data is itself in the form of a fine-grained histogram, i.e. numbers of counts in regular one-second bins. I want to produce a histogram of, say, 10-minute bins (though possibly irregular bins also). I suppose I could re-create a data set as

Dispongo de un experimento en el que cinco tratamientos ha sido aplicados a cinco grupos de voluntarios. En cada grupo había tres personas y a cada persona se le tomaron 3 medidas, En total dispongo de 45 medidas, pero evidentemente no son independientes entre sí. Si no tomo en cuenta que las medidas de la misma persona son más parecidas entre sí (bloques anidados) estaría incurriendo en