similar to: problem in waveslim library?

After playing with the vorbis code for a while and doing tons of hacks and analysis on it, I've found it to perform very poorly with impulse signals. The MDCT seems to cause lots of spreading, and it seems to result in much worse impulse performance then mp3. What is the current plan on handling this? Will a smart quantizer be able to avoid it? I've been looking at various ways of

Dear R Users, For running wavelet functions using dwt( ), modwt( ), and mra( ), a wavelet filter algorithm is applied. For all these functions, default is "la8" and other possibility is "haar". In related documents, another possibilities like as symlet and coiflet ... are not cited. Besides "la8" and "haar", which wavelet filters can be used? Thank

hi everyone . I am trying to use some packages but there are some functions have the same name in different package. for example dwt function both in packages wavelets and waveslim dwt(X, filter="la8", n.levels, boundary="periodic") How can I avoid mixing them up ? -- TANG Jie Email: totangjie@gmail.com Tel: 0086-2154896104 Shanghai Typhoon Institute,China

Can someone please help me understand the examples given in the dwt function of the waveslim library library(waveslim) ?dwt I'm having a problem understanding these lines. I assume they are required because of how the algorythm deals with the signal bounderies. Am I correct in thinking that for wavelets of scale 1 and 2, the coefiecints need to be shifted by 2 places, for scale 3 and 4 shift

Hello, I have upgraded from Mandrake Linux 8.0 to Mandrake 8.1 and try to reinstall my favourite R... Everything is OK for the base software but I have trouble to get some packages installed. Specifically, for some packages, a "collect2" binary seems to be necessary during the compilation/linking process of the library, and LD complains about not finding it : Installing source

Hello I do wavelet transform by using this code: dec=dwt(ld, filter='d8', n.levels=lev, boundary="reflaction"); dec consists of the decomposition coefficients and other How can I change the coefficients the decomposition manualy? -- View this message in context: http://r.789695.n4.nabble.com/wavlet-transform-tp2313427p2313427.html Sent from the R help mailing list archive at

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

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

Airon, I used R2.4.0 on a Windows XP (SP2) (not Chinese :-)) and it still works: > data = read.csv("u:/airon.csv") > xdata = data$Adj..Close > modwt.la8 = modwt(xdata, "la8", n.level=6) > summary(modwt.la8) Length Class Mode d1 1467 -none- numeric d2 1467 -none- numeric d3 1467 -none- numeric d4 1467 -none- numeric d5 1467 -none- numeric d6 1467

Hi: I have encountered problems with imodwt and universal.thresh.modwt and cannot find any reference in R Search. Hope someone can give me some ideas: Starting with modwt.la8 <- modwt(xdata, "la8", n.level=6) <-- this seems to work fine (1) ydata <- imodwt(modwt.la8) will always give ydata as numeric(0) (no values) instead of being a time series data with

Dear all, Using R function "dwt", it seems that I cannot specify the wavelet transformation like Haar. What's the R code for wavelet decomposition which allows me to specify Haar wavelet transformation? Of course, if it can include "db2", that is even better. In general, I want an R function like matlab code "dwt". Thanks in advance! Zhen Li

Airon, I don't think you have to find an English computer 'cause the following must work in your Chinese one :-) Let me explain. First of all, change your lines to xdata <- ckhdat$Adj..Close[1:1447] #names(ckhdwt.la8) <- c("w1", "w2", "w3", "w4", "w5","w6", "v6") note the # sign, i.e., DO NOT change the names

Using ordered probit model, I get errors from dwt and bptest. dwt: Error in durbinWatsonTest.default(...) : requires vector of residuals bptest: Error in storage.mode(y) <- "double" : invalid to change the storage mode of a factor I imagine I have to restate as an individual probit model for each category, but is there an easier way? thanks, bp [[alternative HTML version

Dear WizaRds, unfortunately, I have been unable to replace the '99' and '999' entries in library(UsingR) attach(babies) as definitions for missing values NA, because sometimes the 99 entry is indeed a correct value. Usually, or so I thought, NAs can easily replace a, say, 999 entry via mymat[mymat==999] <- "yodl" in a matrix or data frame. Alas, the babies'

> results <- mra(x$w1mcp, filter = "d4", n.levels = 3, boundary = "periodic", method = "dwt") > write.csv(results, "c:/mydata.csv") Error in as.data.frame.default(x[[i]], optional = TRUE) : cannot coerce class 'structure("mra", package = "wavelets")' into a data.frame I am not able to access data stored in results.

Given a time series of length N, I am trying to figure out its maximum DWT decomposition level. With reference to "dwt" function of R package "wavelets", running the provided example I get the following and wonder how the maximum decomposition level (which is not an integer number) is calculated. I tried myself the formula in the on-line documentation but could not get the

Hi everyone I am using the wavelet and waveslim package in R to find the wave variance.Here is the code return.modwt<-modwt(X, filter="la8", n.levels=5, boundary="periodic", fast=TRUE) return.modwt.var<- wave.variance(return.modwt, type="nongaussian") Where X is a uni-variate time series. I am expecting a matrix with 5 rows(no. of levels) and 3

waveslim 1.4 has recently been uploaded to CRAN and is fully compatible with Rv2.0. Besides ensuring usability with the most recent version of R, two additional "flavors" of wavelet methodology have been added to the package: (1) Hilbert wavelet pairs and (2) the dual-tree complex wavelet transform [only 1D and 2D ported from Matlab code by Selesnick]. The dual-tree CWT code has

waveslim 1.4 has recently been uploaded to CRAN and is fully compatible with Rv2.0. Besides ensuring usability with the most recent version of R, two additional "flavors" of wavelet methodology have been added to the package: (1) Hilbert wavelet pairs and (2) the dual-tree complex wavelet transform [only 1D and 2D ported from Matlab code by Selesnick]. The dual-tree CWT code has

Airon, I'm not sure I have understood your question. Anyway, it seems to me that you'll have to code a little. Take a look at ?modwt. I think you'll have to put zeros each scale a time except the one you want to reconstruct. Then do imodwt. After some small loop, I think you'll get the desired result. HTH, Rogerio. ---------- Cabe?alho original ----------- De: "Airon