similar to: autocorrelation and levinson-durbin

> 3) a more general question: > i have not been able to find any reason why > 10 lpcs are used. i suppose 10 lpcs are > enough for prediction and using more coeffs > would not have made too much difference. > and 10 is even, good for lpc->lsf. had read > somewhere "historically", during the analog > era, this number is used. or maybe 10 is analagous > to

Hi Jean-Marc I'm looking at the 1.0.5 source, and I'm not seeing an _spx_lpc(). There's an _spx_autocorr(), which is in lpc.c and is called near the start of the encoder function in nb_celp.c. The encoder seems to call the autocorr() function, then calls wld() to do something called Levinson-Durbin. Am I right in thinking that after the call to wld(), the st->lpc[] array

Hi everyone! It's my first time posting to this list, and I've got a fairly technical question. I'm interested in doing phoneme extraction, and one of the first steps in the algorithm I'm planning to use is to get the LPC coefficients for an input frame. Since Speex is CELP-based, the coefficients must be generated in there somewhere. I've tried digging around in the source

Bonjour, D?apr?s votre question ? Jean_Marc : ? Je m'int?resse ? la source 1.0.5, et je ne vois pas une _spx_lpc (). Il ya une _spx_autocorr (), qui est en lpc.c et est appel? ? proximit? du d?part de la Codeur fonction dans nb_celp.c. L'encodeur semble appeler la autocorr () la fonction, puis appelle wld () pour faire quelque chose qui s'appelle Levinson-Durbin.

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

hi, thanks for answers to my previous qns. have some more of them. hope it's ok to ask questions on the basics of CELP here. let me know if it's not. 1) synthesized filter stability after interpolation of LSFs I read from some resources that if LSF representation of LPCs is used for interpolation, stability of the synthesis filter is guaranteed. from another source, i read that the

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Can someone point me to literature and/or R software to solve the following problem: Assume n true scores t measured as x with uncorrelated errors e , i.e. x = t + e and assume each true score to a have a certain amount of correlation with some of the other true scores. The correlation matrix cx of x will have its off-diagonal entries reduced by measurement error compared to the true

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <-

Hi list! My Problem: all calls to international numbers will be dropped after exactly 15 minutes... I have a VoIP-account by Deutsche Telekom. This is what I see when I call someone (my parents) and the connection will be dropped: == Using SIP RTP CoS mark 5 -- Executing [+39015222222 at default:1] Set("SIP/00493511111111-00000125", "newNumber=0039015222222") in new

Should "dwtest" and "durbin.watson" be giving me the same DW statistic and p-value for these two fits? library(lmtest) library(car) X <- c(4.8509E-1,8.2667E-2,6.4010E-2,5.1188E-2,3.4492E-2,2.1660E-2, 3.2242E-3,1.8285E-3) Y <- c(2720,1150,1010,790,482,358,78,35) W <- 1/Y^2 fit <- lm(Y ~ X - 1) dwtest(fit,alternative="two.sided")

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

Hi, How could I use R to check Durbin Watson Test Bound? Best, Rebecca

Sir,   I am unable to get the source code for Durbin-Watson test, as I want to calculate the p-value for Durbin Watson statistic using interpolation method. I sent this mail to r-help, but it was rejected, please suggest me some way. I will be highly greatful to you. Thanks in advance Ramesh [[alternative HTML version deleted]]

Hi, I ran an experiment with 3 factors, 2 levels and 200 replications and as I want to test for residuals independence, I used Durbin-Watson in R. I found two functions (durbin.watson and dwtest) and while both are giving the same rho, the p-values are greatly differ: > durbin.watson(mod1) lag Autocorrelation D-W Statistic p-value 1 -0.04431012 2.088610 0.012 Alternative

Dear R People: Do any of the linear model or regression function calculate the Durbin-Watson test statistic for serial correlation, please? I found the hat matrix, studentized residuals, and so on, but no D-W. Thanks so much! Sincerely, Erin M. Hodgess, Ph.D. Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown One Main Street Houston, TX 77002

Hi, I have two time series, y and x. Diff(y) and Diff(x) both show no autocorrelation. But durbin.watson(lm(Diff(y)~lag(Diff(x),k=-4)) gives a DW value of zero. How come the residule is autocorrelated while Diff(y) and Diff(x) are not? Does anyone know if in my case a DW of zero indicates serial correlation, or is it telling me that the DW statistics is not the appropriate statistics to use here?

Does anyone know where I can get R code for plotting the Brown , Durbin and Evans cumsum procedure ( 1975 ) ? I wrote my own code but I am a little worried that my confiodence bands may not be correct ( I find the formula in the original paper confusing and S+Finmetrics has a formula but that formula implies that there should be 4 lines as far as I can tell ) so I would like to see someone

Dear all, I am new here ,I attempted to use R to estimate the spatial Durbin (mixed) model,and mydata is a panel data form,and the matrix is generated by geoda software ,here is my Command and error,really hope your help ,thank you! #??gal library(spdep) w<- read.gal("E:/splm/zj.GAL",override.id=TRUE) ww<-nb2listw(w,zero.policy=TRUE) #???? library(foreign)

Does R have a function for the Durbin-Watson test? ......................................................... Steven Scroggin scrog at lvcm.com -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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",