similar to: Discrimination of almost-random time series

Greetings all, I'm working on a project trying to apply fourier analysis to timestamped router logs, using R to perform the analysis. The idea is to determine if any type of traffic (say, outgoing ICMP requests) has strong periodic features because it may indicate a compromise somewhere on the network. The FFT requires all data points to be evenly spaced, but the recorded events do not

Hi, I have recently used the CTS package in order to use the Lomb-Scargle periodogram (spec.ls) function. I have noticed an issue that I hoped you may be able to explain. If a regularly spaced time series has two points removed, one at either side of a single data point (thus making an irregularly spaced time series), a spectrum with a very large peak at the highest frequencies is produced. An

Hi, Does anyone know the name of the package that includes a function for computing the lomb periodogram on irregular spaced ts data? I saw the package once ~ 1 month ago but cannot find it now ... , Rich

Greetings all, I am using Fourier analysis to search for periodicities in IP network traffic by generating periodograms and then visually examining them for large, distinct peaks. However, in many cases it is not readily apparent where there are periodicities. I have no experience with discrete maths so I've come up against a block here: How do I define what the "noise floor"

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] *

hi R users I have a large 1D dataset and some of them is NA value . I found I cound get the spectrum by such a command. ua=c£¨10£¬30 £¬40£¬50£¬NA£© spectrum(ua) and I could not use na.rm just like mean or sd function How could I get the spectrum of ua ? thank you . -- TANG Jie [[alternative HTML version deleted]]

I'm evaluating * to replace the crap set of peered "smart" phones we have now in our small office, but I haven't been able to find out about this anywhere yet: I need to know if * can discriminate _incoming_ FAX calls on a voice line and route them to a specific extension? We have a little standalone box to do this now, but only for one line, and if that line is busy---we

Background: OS: Linux Mandrake 9.1 release: R 1.9.0 editor: Xemacs 21.4 frontend: ESS 5.1.23 --------------------------------- Colleagues I have two time series (upwelling index and water temperature) of evenly spaced, daily data over 18 months, but the upwelling index series has a gap of about 2 months right in the middle of it. I want to do the acf, pacf, ccf, and a cross-spectral analysis

Hi everybody, I am reading the Lomb paper (Lomb, 1976) and I found an interesting equation, and I wish to resolve it using R. I am wondering if anybody has a hint. The equation is: Yis[i] = a*cos((2*pi/T)*(Times[i] - Tau)) + b*sin((2*pi/T)*(Times[i] - Tau)) ... (1) Where T and Tau are constants. I know the "Times" and "Tis" values (in fact these values come from a Time

Dear R users Is there a function in R to make a peridogram for a spectral analysis of unevenlly sampled data?? something like spec.lomb() for S-Plus?? How to plot a vector with unequally spaced time series?? e.g day/month/year V1 03/08/82 0.34 28/08/82 1.42 12/09/82 0.28 20/09/82 0.56 03/10/82 0.85 21/10/82 1.45 thanks -- Marcelo Alexandre Bruno - Pos-graduacao Oceanografia Biologica

Hello to everybody, I just subscribed to this list. I find R a really nice environment for statistical analysis. I started to use it some days ago. I use it at home in my Linux box, but I would like to use it in my institute in my Digital Alpha (my machine runs OSF1 V4.0). I tried to compile the distributed source code, without success due to some errors. And the binary available is in RPM

I have been working on fitting Cox model for prediction by using rms package. I want to measure model's calibartion and discrimination. Discrimination was measured by using validate() in rms, Dxy can be transferred to Harrell's c index. But in this way, I cannot get 95%CI of c index. How can I do this in R? And by the way, what value should be in c index to present the model's well?

Hello, Could you tell me if a package exists to perform a binary kernel discrimination using a training set compose of molecules represented by binary fingerprint. This method was first described by Harper in J. Chem. Inf. Comput. Sci 2001 41 1295 and is also described in recent papers published in the same journal by Hert Jerome. I have attached the page describing the BKD method used in the

I've got a sample set of variables x1,...xn and a factor f that classifies each sample to belong to either group 0 or 1. If I build a model with m1<-glm(f~. , family=binomial(link="logit"),data=frame); pv<-as.vector(predict(m1)) prob<-plogis(pv) does then "prob" predict the probability of a sample to belong to group 1? Is this equivalent to logistic

Hi there, I can't seem to wrap my head around the differences between discrimination and calibration. I think that I learn best by examples. Could someone provide me with detailed explanations using examples of when a model could do both well, both poorly, and one well and the other poorly? Thanks! [[alternative HTML version deleted]]

Dear All, I am looking at discriminating among several individuals based on a few variable sets (I think some variables do not make sense unless they are entered together, so I "force" them into the models together, hence datasets). I have done so with linear discriminant analysis (LDA) using "MASS::lda", with acceptable results. However, one of my collaborators

Hello, I am using logistic discriminant analysis to check whether a known classification Yobs can be predicted by few continuous variables X. What I do is to predict class probabilities with multinom() in nnet(), obtaining a predicted classification Ypred and then compute the percentage P(obs) of objects classified the same in Yobs and Ypred. My problem now is to figure out whether P(obs) is

Dear List, I'd like to run a discriminate analysis on a data set, but have no idea how to go about this in R. I have attempted to locate info in the manuals, but may not be consulting the right sections or documents. Can anyone point me to appropriate documentation if such exists. Many thanks, David S. David White sdavidwhite at bigfoot.com Columbus, Ohio

I have a target whose calling conventions specify that pointer-typed arguments are passed in different registers than same-sized integers. It appears that in the SelectionDAGBuilder, arguments/formals with pointer type are lowered to the corresponding integer MVT (via this path: SelectionDAGISel::LowerArguments llvm::ComputeValueVTs TargetLoweringBase::getValueType

I apologise for writing again about the problem with using stepAIC + multinom, but I think the reason why I had it in the first place is perhaps there may be a bug in either stepAIC or multinom. Just to repeat the problem, I have 126 variables and 99 cases. I don't know if the large number of variables could be the problem. Of couse the reason for doing a stepwise method is to reduce this