similar to: Outliers detection using wavelets

Hi R users   Thanks in advance   Is lifting wavelet transform implemented in R? If so, which package or codes  can be used for performing that?    assaedi76@yahoo.com   Thanks     [[alternative HTML version deleted]]

Hi!! I'm looking for a package in R for time series analysis using wavelets( I'm interested on extractingt information from electrocardiograms with the aim of study the heart rate variability) so i want to know which package is the best for me. I've thinking about using some of those(you can recommend me others,xD): *wavelets: A package of funtions for computing wavelet filters,

Hi, this is both a statistical and a R question... what would the best way / test to detect an outlier value among a series of 10 to 30 values ? for instance if we have the following dataset: 10,11,12,15,20,22,25,30,500 I d like to have a way to identify the last data as an outlier (only one direction). One way would be to calculate abs(mean - median) and if elevated (to what extent ?) delete the

I need to detect outliers in a large data set which is highly right-skewed. I plan to use medcouple-based outlier detection. Is there any support for medcouple-based outlier detection in R? Are there any other routines in R to perform outlier detection in highly right-skewed data? Manuj Sharma See the Web's breaking stories, chosen by people like you. Check out Yahoo! Buzz.

Hello, I use wavethresh packages to perform wavelet analysis. In particular, I would like to compare 2 signals (vectors) after a wavelet decomposition. I would like to use cor.test function, but this function acts on the entire vector values. I plan to perform a cor.test on each level of the wavelet decomposition, say N. So I will have at the end of a first step N results of cor.test. How can

Last week there was a thread on outlier detection. I came across an article which has a very interesting paragraph. The article is Missing Values, Outliers, Robust Statistics, & Non-parametric Methods by Shaun Burke, RHM Techology Ltd, High Wycombe, Buckinghamshire, UK. It was the fourth article in a series which appeared in Scientific Data Management in 1998 and 1998. The very

Hi friends, How to detect outliers and high leverage points for GLM ? Could I use plot(model) (i) "Residuals vs Fitted" graph to detect the outliers ? (ii) "Residuals vs Leverage" graph to detect the high leverage points ? And then remove those points from the data and re-run the model ? [[alternative HTML version deleted]]

Suppose I am reading data from a file and the data contains some outliers. I want to know if it is possible in R to automatically detect outliers in a dataset and remove them -- View this message in context: http://n4.nabble.com/How-to-detect-and-exclude-outliers-in-R-tp1017285p1017285.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have been using the lqs function with method='lms'. However the results I get are a little different from the results noted by Rousseeuw & Leroy (Robust Regression and Outlier Detection) and I was wondering how to use these results for outlier detection. I'm using the stackloss dataset, for which the original Rousseeuw et al. program points out that observations 1,2,3,4

I am having trouble plotting outliers on time series. Give then following code: ############################################################ # find STL Outliers by weight and append sh2, use Robust # this should allow the initial outliers to be filtered # this section may be commented out. ############################################################ tsSourceDiag <-

I have written a short script to estimate the size of a test of non-constant mean in an AR(1) time series. When I run the simulation on my PC (R version 2.7.1), I get the expected result: an empirical size much larger than the nominal size. On the Red Hat machine (R version 2.7.2) in my department, however, I get p-values > .45 for every simulated test. Below is my simulation code (uses

I take the liberty of pasting the on-line documentation for package "wavelets", functions "dwt" and "wt.filter", pertinent to the wavelet/filter choice. While I apologize for my verbose messages with huge attachments, I encourage whoever loads packages to CRAN to take some minutes to reread the attached documentation. Usage dwt(X, filter="la8", n.levels,

Currently I find that if I call stl() repeatedly I can use the weights array that is part of the stil output to detect outliers. I also find that if I repeatedly call stl() (replacing the outliers after each call) that the "remainder" portion of the stil output gets reduced. I am calling it like: for(.index in 1:4) { st <- stl(mt, s.window=frequency(mt), robust=TRUE)

Dear [R] people, I am looking for a method to test if there are any changes (jump points) in a time series data set or not. If there are, I would like to identify the jump points. All the data is available from the beginning so an offline method could be used. The current data set that I have could be seen as a time series with five almost equally sized segments that stands out to have different

Hello R users, Can someone tell if there is a package in R that can do outlier detection that give outputs simiilar to what I got from SAS below. Many thanks in advance for any help! Outlier Details Approx Chi-

Does anyone know of any use of wavelets (particularly for density estimation) in R? Are there any wavelet packages? -Alex Morgan -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject

Dear list, in short: I would like to calculate the mean frequency of a signal (e.g. time series) using the wavelet transform. with details: I did not find a R function to calculate a mean frequency using one of the cran packages. My searches using R Site Search returned: **(1)waveclock {waveclock} R Documentation Reconstruction of the modal frequencies in a time series using continuous

Hi, I am interested in a applying wavelets as a smoothing tool for my (1-dimensional) data. I looked into wavetresh and waveslim packages but could not quite figure out an obvious way to do this after running dwt or wt functions. Would someone be able to point me in the right direction on how to denoise univariate data using one of wavelet packages available in R? Thank you very much Jane ps

<WARNING: Long message ahead> Well, I have actually done something the past 1 1/2 week. I've created a program that runs several filters over an image to extract edge information. Currently it loads any uncompressed grayscale TGA file, and spits out another uncompressed greyscale TGA file that is 255 at places where there are edges, and 0 where there are not. I managed to get out quite

Hi, I was looking for a package that would help with outlier detection for bimodal distributions. I have tried 'outliers' and 'extremevalues' packages, but am not sure if they are ok for bimodal distribution. Any help would be highly appreciated! thanks, [[alternative HTML version deleted]]