similar to: Record inbound and outbound calls to and from one number.

Ok, I thought the point of asterisk@home was that it automatically detected the X100P board and configured it correctly. Is this incorrect? You still need to modify /etc/zaptel files? And not just using the AMP configurator. There is no mention of this on the Asterisk@home webpage. Can anyone who has actually used ast6erisk@home confirm this one way or the other? Thanks, Dean

Dear R-Users I have the following matrix > out$desc [,1] [,2] [1,] "" "" [2,] "y_{01}(k-001)" "" [3,] "y_{01}(k-002)" "" [4,] "y_{01}(k-003)" "" [5,] "u_{01}(k-001)" "" [6,] "u_{01}(k-002)" "" [7,] "u_{01}(k-003)" ""

I'm trying to install asterisk@home, I've just downloaded the latest cd from soundforge. I can get it to install ok (network card didn't auto configure - but I worked out how to use 'netconfig'). I worked out how to add a few grandstream budgetone fine. Worked out how to upload music etc. Worked out how to modify FOP. Voicemail and meetme's work fine.

Hi, This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

Dear R-Users, I have the following problem to solve and I wonder if there are means in R that can help me. At irregular time intervals I observe a random walk process, Y, with time-varying drift. I assume that the drift, D, is a (linear) function of some parameter X. The goal is to estimate D(X). I could regress Y_{t+dt} - Y_{t} ~ X, but it's probably not appropriate since Var(Y_{t+dt} -

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly

Dear all this is a question of model specification in lme which I'd for which I'd greatly appreciate some guidance. Suppose I have data in long format gene treatment rep Y 1 1 1 4.32 1 1 2 4.67 1 1 3 5.09 . . . . . . . . . . . . 1 4 1 3.67 1 4 2 4.64 1 4 3 4.87 .

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

Dear people, I'm running RedHat 9.0 and R : Version 1.7.1 (2003-06-16) from the help file # Usage: # # filter(x, filter, method = c("convolution", "recursive"), # sides = 2, circular = FALSE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Here is a test case: extern double floor(double); extern double floor_(double); double x = 1.5; double y, y_; void foo() { double y = floor(x); double y_ = floor_(x); } If I compile this for Mips16, it calls the proper helper function for floor_ but not for floor, because the signature for floor in callee info is wrong. Args[0] = void RetTy = void

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Dear R Users, Is the p-value reported in a two-tailed binomial exact test in error or is it a feature? If it is a feature, could someone provide a reference for its two-tailed p-value computations? Using Blaker's (2000 - Canad. J. Statist 28: 783-798) approach,the p-value is the minimum of the two-tailed probabilities $P \left(Y\geq y_{obs}\right)$ and $P\left(Y\leq y_{obs}\right)$

Hi, I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows: Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t restriction a1+ a2 =1 Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them. Thank you for your help Ahmad Abu Hammour --------------

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple