similar to: A question about linear optimizaton

I'm running Asterisk 1.6.2.5 with chan_skype on a x64 linux platform. When a user calls from skype (not skype-in) to asterisk, dtmf (basically menus for a conference system) works just fine. But when a user from the inside (soft or hardware sip phone) calls out via skype-out dtmf doesn't work. I have tried setting the codec to alaw, and dtmfmode to all possible options (auto, inband and

Anyone have any ideas why DTMF would not work on only one number? Looking through the logs, anytime a button is pressed, this is what shows up: 2006-04-13 11:39:18 DEBUG[22441] chan_zap.c: Exception on 9, channel 1 2006-04-13 11:39:18 DEBUG[22441] chan_zap.c: Got event Dial Complete(9) on channel 1 (index 0) 2006-04-13 11:39:18 DEBUG[22441] chan_zap.c: Echo cancellation already on We

I am aware of how to match a particular caller-id or a caller-id pattern and do something with the call like this: exten => 15554441212/_888NXXXXXX,n,Playback(GoAway) What I am curious about, is the best way to block unknown, private and 000-000-0000 calls. I know I can do this for 000-000-0000 calls: exten => 15554441212/0000000000,n,Playback(GoAway) Is there a better way to catch

need transcoding to a|ulaw. I am using it with no problems (have g729 licenses as well though). A bit off topic, I have found some extra configuration that is not really in the docs (or I could not find them): fullname=Your full name country=gr language=en city=City province=Province phone_home=+fullinternationalnumber phone_office=+fullinternationalnumber email=your at email.com

IMHO! I just see a skype channel as something good for asterisk. Skype has broad coverage. I can't imagine that skype wouldn't be interested in selling corporate accounts "skype trunk lines". Imagine having unlimited or X amount of continious calls coming in on SkypeIN and out on SkypeOUT from Asterisk. Internal Phones would all talk IAX or SIP to asterisk and use all PBX

Hello folks, Any ideas how to do this? data.frame is a data frame with column names "x1",...,"xn" y is a response variable of length dim(data.frame)[1] I want to write a function function(y, data.frame){ lm(y~x1+...+xn) } This would be easy if n was always the same. If n is arbitrary how could I feed the x1+...+xn terms into lm(response~terms)? Thanks Richard -- Dr.

Hello all, I am trying to configure a linux box to make some QoS into my netowork and, at the same box, control my clients bandwidth. I have this classes created: ---------------------------------------------------------------- UP="eth0" # wan infocontabil DL01="eth2" # lan clientes $TC qdisc del dev $DL01 root 2> /dev/null >

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

hello, I try to use layer7 filter to classify packets. I have a proble with http match. This protocol seems to work well with l7-filter (http://l7-filter.sourceforge.net/protocols) but for me nothing is filtering in http class. Someone can help me ? Here is my script : #!/bin/bash IPT_BIN=/sbin/iptables TC_BIN=/sbin/tc INTER_OUT=ppp0 LINK_RATE_UP=1000Kbit RATE_ACK=200Kbit RATE_DEFAULT=100Kbit

We are in the process of preparing to move our Asterisk server to a Digital T1 interface card instead of a analog card (via an Adtran which is now connected to the T1). I did a preliminary test the other day and hooked the T1 line up to the T1 card, bypassing the Adtran. This worked rather well I must say. The two issues I ran into are: 1) Caller ID is not working even though I enabled

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

He, I used the "coxph" function, with four covariates. Let's say something like that > model.1 <- coxph(Surv(Time,Event)~X1+X2+X3+X4,data=DATA) So I obtain the 4 coefficients B1,B2,B3,B4 such that h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4). When I use the function on the same data > predict.coxph(model.1,type="lp") how it works in making the prediction?

I have a couple quick questions about the use of cv.glm for cross-validation. 1. If we have a Poisson GLM with counts Y~Poisson(mu) and ln(mu)=beta0+beta1*x1+..., is the prediction error (delta) that is output from cv.glm provided in terms of the counts (y) or the (mu)? 2. Can cv.glm be used for negative binomial models fit using glm.nb? It appears to work, but since NB models aren't

Hi dear R users, Is it basically correct that a problem is ( linearly on nonlinearly ) modeled so that the slack variable is bounded by an upper bound ? If so, how it can be handled and coded practically ? for example: x1+ x2 =< b so ----> x1 + x2 + s=b s=b- x1 - x2 b- x1 - x2 =< upper value But algorithms can not calculate b- x1 - x2 , because

Hi everyone, How can i obtain R2 for SAR model? and how can i validate the results, can i use the coefficients directly in a simple formula like y=b0+b1*x1+... or do i have to use the complicated formula for SAR (the one with the weight matrix and rho and...)?   Thanks for any help!  [[alternative HTML version deleted]]

Hello I'm trying to do the following vector operation: given vector x = c(x1,x2,x3,x4...xn), produce vector y = c(x1,x1+x2,x1+x2+x3,...x1+...+xn). E.g., from x = c(1,3,2,2,5), produce y = c(1,4,6,8,13). The underlying problem is finding the cumulative distribution function given the empirical density distribution function. I have done some research on this but the only relevant

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Dear All, Here is my reproducible example: > library(stargazer) Please cite as: Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.2. https://CRAN.R-project.org/package=stargazer > x1=1:1000 ; y = 2 * x1+ rnorm(1000) > stargazer(lm(y~x1)) Error in (.format.s.statistics.list != "p25") &&