search for: whiten

Hi all, I am whitening my data: # code begins N <- 300 M <- 2 x <- matrix(data=rnorm(N*M, 0, 3)-10, ncol=M, nrow=N) y <- matrix(c(1,-2,-2,1), ncol=M, nrow=M) z <- data.frame(x %*% y) colnames(z) <- c('x','y') par(mfrow=c(1,3)) plot(z, pch=5, col="blue") whiten <- functi...

...got the following error: Error in ar.ols(x, aic = aic, order.max = order.max, na.action = na.action, : 'order.max' must be < 'n.used' I am pretty sure, that the error comes from the NeweyWest function in line 45, as the NeweyWest function uses the ar.ols() function for pre whitening. Does anyone has an idea how to circumvent this? Can I just shut off pre whitening? I am thankful for every suggestion. Simon [[alternative HTML version deleted]]

Hi R users, I am doing cross correlation analysis on 2 time series (call them y-series and x-series) where I need the use the model developed on the x-series to prewhiten the yseries.. Can someone point me to a function/filter in R that would allow me to do that? Thanks in advance for any help! -- View this message in context: http://www.nabble.com/Whitening-Time-Series-tp22041765p22041765.html Sent from the R help mailing list archive at Nabble.com.

...ze="2"> Hi there,</P> one step in the encoding process (if I got it right), would becomputing a "floor curve" (according to the psychoacoustic model)<BR>and then subtracting this curve is from the MDCT data.</P> It's said that this works like a "whitening filter".</P> Could somebody please explain, how this is meant ?</P> I know, that a whitening filter removes the non-whitecomponents from a noise signal. But how can this be adopted<BR>to MDCT data ?</P> Is it like subtracting only the amplitude values, or some kin...

I have a time series with a one year lag, ar=0.5. The series has some interesting events that disappear when the series is whitened (i.e., fitting an AR process and looking at the residuals). I'd like to remove the autocorrelation in stages to see the effect on the time series. Is there a way to specify the autocorrelation term while fitting an AR process? For instance, given the following: x <- arima.sim(model = li...

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Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75 However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if there is any di...

Running dovecot 1.0.5 on Debian testing. I've just tried to create folder using Russian charters after that I'm tried to renamed it without luck. Folder removed and no new created and I still subscribed to old folder even its not exist anymore.

Hi, Can you please help me? Whenever I try to run f.ica.fmri.gui(), R crashes. In the GUI, I selected an *.img file and I checked the 'Create mask' box and I typed a name for the object and then I press 'Start' button. Then in the R Console window it said 'Reading...Whitening', but after the Whitening message an error window popped up and then R crashed. Did I do something wrong? I tryed to do same thing in the computer at the lab and in my computer, I got same result. I appreciate your help. Regards, Seungshin Rhee [[alternative HTML version deleted]]

I have followed a combination of instructions from this post and have been able to get an Ubuntu paravirtualized instance running under snv_96. Is there a way to "list" the fact that it is paravirtualized? For example, xm list, virsh list, etc. thanks -- This message posted from opensolaris.org

Dear all, Could you pleas advise me on the following? I need to use general(ized) linear models (binomial distribution + logit link function) , to describe the preferred environment of each species (each sample is an individual in which I have measured several variables and also recorded the species it belongs to) However, must account for the spatial autrefoocorrelation between

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...like to use the model to conduct an intervention analysis. The problem is that, when I include a step function in auto.arima, by including a binary variable in "xreg", the arima order that auto.arima gives is different from when I don't include it. From my understanding, the "pre-whitening" that is required for intervention analysis simply means to determine the (p,d,q) BEFORE adding exogenous regressors. Would auto.arima not do this first, and only then apply the exogenous regressors? If this is the case, I assumed that the arima order would be the same for whether I include...

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...thing as a suggested value, every number is wrong to some degree. The quality is an estimation of how random your source really is; it's a property of your hardware. If the device is virtual you're in trouble ;-) Either you have a way to query the underlying real hardware, or you do your whitening and entropy estimation in the backend driver -- then you can claim 100%. Torsten

...thing as a suggested value, every number is wrong to some degree. The quality is an estimation of how random your source really is; it's a property of your hardware. If the device is virtual you're in trouble ;-) Either you have a way to query the underlying real hardware, or you do your whitening and entropy estimation in the backend driver -- then you can claim 100%. Torsten

Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10

Hello list, I've been reading through the archives and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme

We have a 2008r2 AD domain. We join Linux machines as domain members using Samba with Winbind (I'll show all of my config files below). This portion of our setup works without failures of any kind. However, some of these machines are web servers for Intranet stuff and we'd like to have SSO working. For this, we use Apache (HTTPD) plus mod_auth_kerb (requires a keytab file). So, since

Could anyone tell me whether there is an R function for removing autocorrelations from a series of observations before performing a linear or nonlinear regression analysis on them? Many thanks, Andrew Wilson