similar to: Vorbis and Joint Stereo.

Hello, I've been postponing some of my encoding for when joint stereo gets implemented. The reason I've been doing this, is that I am under the impression this is the largest step in the quality/bitrate ratio that's left. Now I'm wondering if I am correct in thinking this. lame's documentation seems to imply it doesn't make much of a difference at higher than 128kbps (I

I've got an idea for joint multichannel coding. Here is my idea: My idea is a process for coding 2,3,4 or 5 combined channels, in a compatible way for decoders unable to deal with combined channels. part1: All channels to be coded are summed, each one effected by a coefficient related to its sound power importance for the listener. The sum of all channels is devided by the square root of the

Hello all, I created today a fake stereo piece of music (the left and right channels were completely equal), and encoded it with the all the modes the RC2 encoder supports, and found that: * * dual * waste * waste * * mode * joint * stereo * in * in % * * * stereo * /2 * kbits * of JS *

Suppose I make two discrete variables -- > D <- data.frame(f1=sample(1:5,100,replace=T), f2=sample(1:5,100,replace=T)) I know I can do: > table(D$f1, D$f2) 0 1 2 3 4 0 5 5 5 5 4 1 4 2 6 7 3 2 5 3 5 3 6 3 3 1 3 1 2 4 6 4 3 3 6 > table(D$f1) 0 1 2 3 4 24 22 22 10 22 > table(D$f2) 0 1 2 3 4 23 15 22 19 21 which is all great. But how do I produce the

Dear all, Given rr<-data.frame(r1<-rnorm(1000,10,5),r2<-rnorm(1000,220,5)) How can I add a column (rr$p) for the joint probability of each r1 & r2 pair? I know how to add the column.. I just dont know how to compute the p value for joint probabilities given the two samples. //M

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

Hi all, I'm fairly new to R, so I'm still trying to feel out what is available to me. I would like to be able to plot joint density in a two dimensional plot where density is indicated by color or darkness gradients, like a 2d color coded topographic map. Ideally, the output would be something I could then plot other points or lines on. Currently, I'm calculating joint density with

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

Dear R users, For a uni-variable distribution represented in a numerical vector, we can obtain a distribution function using 'ecdf', and then calculate corresponding p-values. But if I have a 2-column dataframe representing a bi-variable joint distribution, given a pair of values, how can I get the p-value? And how can I plot out the density of the joint distribution? Best

Hello, I am interested in fitting a segmented model with unknown joint points in nls and perhaps eventually in nlme. I can fit this model in sas (see below, joint points to be estimated are a41 and a41), but am unsure how to specify this in the nlm function. I would really appreciate any suggestions or example code. Thanks a lot. -andy proc nlin data=Stems.Trees; params b41=-3 b42=1.5

Dear colleagues, I have a dataset that looks as below. I would like to make a new dataset that excludes the cases which are joint conjunctions of particular state names and years, so Connecticut and 2010, Maryland and 2010 and Vermont and 2010. I'm trying the following subset code: newdata<- subset(bpa, (!State=="Connecticut" & year<"2010")) It appears that

Dear all, Please, look at the following code: attach(geyser) f1 <- kde2d(duration, waiting, n = 5) a <- 0 for (i in 1:5){ for (j in 1:5){ a <- a + f1$z[i,j] } } As far as I understood from Help kde2d returns matrix elements of which are values of joint probability mass function Pr(X=x,Y=y) therefore, sum of its elements should sum to 1. Which is not the case from my check. Where is

Dear R family, I want to calculate the joint probability (distribution) of two random continuous variables X and Y. Could to please tell me how to do it?Thanks in advance.. elisa [[alternative HTML version deleted]]

For a homework question. I was wondering if rcmdr has a function to plot a graph of a bivariate function of X and Y. I have a function with joint pdf fX,Y(x,y) = x+y for 0<x<1 , 0<y<1 I've tried > x <- seq(0,1,.001) > y <- seq(0,1,.001) > r = x+y > plot(r) but it seems to just add them together say .2+.2 .3+.3 not other possibilities like .9 + .1 Thanks

Hi, Can someone set me straight as to how to write formulas in R to indicate: complete independence [A][B][C] joint independence [AB][C] conditional independence [AC][BC] nway interaction [AB][AC][BC] ? For example, if I have 4 factors: hair colour, eye colour, age, sex does > mosaicplot( frequency ~ hair + eye + age + sex) mean that the model fitted is of complete independence of

Hello list. I need help (e.g., a reference, code, package, etc.) in calculating the joint entropy of many variables (some sure highly mutually-informative and some not). Is there anyone here who knows a computationally-efficient solution (such as an R package)? I appreciate you help ... Best, Reza [[alternative HTML version deleted]]

X and Y are independently distributed random variables. I would like to study the contours of the joint density of these two variables. Any function to call? Thank you very much! -- View this message in context: http://www.nabble.com/How-to-plot-contours-for-joint-density-of-2-independently-distributed-r.v.--tp19493126p19493126.html Sent from the R help mailing list archive at Nabble.com.

Dear All, I am estimating a Cox proportional hazard model, with several interactions of the type a*z + a*y + a*x + b*z + b*y + b*x. I need to know if the first three (the "a"s) are jointly significantly different from the last three (the "b"s). I have tried several approaches, but have been unsuccessful. Here's the model, and the code I came up with, with the obvious

Dear All, I was wondering if it is possible to generate a regression summary (it does not matter at this stage if from an lm or for example a glm estimate) in which to obtain the joint significance of a set of regressors? Examples could be looking at the joint significance level of a polynomial, or of a set of exogenous variables of which is of interest the linear combination suggested by the

Hi, If I want to encode two audio channels that are not related, audio from two different sources not stereo, is it best to create two instances of a single channel encoder or use one encoder with two channels? thank you, Paul.