similar to: chan_h323 and extensions.conf

Hello all, Encouraged by the successful "demo", I'am getting on with Asterisk CVS. I added 2 H.323 extensions in extensions.conf [default] include => demo exten => 701,1,Dial(H323/gm2@192.168.1.20/s) exten => 702,1,Dial(H323/gm2@192.168.1.25/s) With: - [demo] is defined by default in sample.extensions.conf - Asterisk server is running on host 192.168.1.20, on the

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

Dear All Sorry for what appears a trivial matter - I'm new to R and am stumbling ahead. I have a table of numerical data (36 rows by 12 columns) such as below: GM1 GM2 GM3 GM4 GM5 ...etc GM12 Run1 1 2 1 2 3 ... Run2 2 1 3 2 1 ... ... Run36 2 1 1 1 1 I would like to plot simple line graphs of some of the runs or

Hello I am not really a statistic person, so it's possible i did something completely wrong... if this is the case: sorry... I try to get the best GLM model (with the lowest AIC) for my dataset. Therefore I run a stepAIC (in the "MASS" package) for my GLM allowing only two-variable-interactions. For the output (summary) I got a model with 7 (of 8) variabels and 5 interactions and

Hello all, I'am trying to use 2 gnophones on my LAN. But I can't get any sound. Here is my configuration: 1. on PC1, I have: - Asterisk compiled from CVS (CVS-05/15/03) and it runs. - Gnophone binary version from Debian: 0.2.4+cvs.20020624-3 - a sound card working ( module es1371 for /dev/dsp) - I registered it as "alice" in extensions.conf 2. on PC2, I

Hi all, I want to make extra columns in my datafile where the id of every groupmember is mentioned in separate columns. To explain it better see the example: id<-c(1,2,3,4,5,6,7,8,9,10,11,12) group<-c(1,1,1,1,2,2,3,3,3,3,3,3) a<-as.data.frame(cbind(id,group)) a id group 1 1 1 2 2 1 3 3 1 4 4 1 5 5

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

Dear list, I am trying to use MuMIn to compare all possible mixed models using the dredge function on binomial data but I am getting an error message that I cannot decode. This error only occurs when I use glmer. When I use an lmer analysis on a different response variable every works great. Example using a simplified glmer model global model: mod<- glmer(cbind(st$X2.REP.LIVE,

A few general comments about stepwiseAIC and a suggestion of how to select models a) Apart form the problem, that stepwise selection is not a garanty to get the best model, you need to have a lot of data to avoid overfitting if your model includes 7 parameter plus interactions (> 10 observations per parameter is what you are ideally looking for). b) Have a look at Anderson and Burnham's

Hello all, It's me again. I would like play with calls between a H.323 client and a SIP client through * inside my LAN. For that, on host 192.168.1.20, I use GnomeMeeting (GM20) and Asterisk; on host 192.168.1.25, I use SJphone (SJ25) as SIP client on Windows and I register into * with a username, no password. The 3 files oh323.conf, sip.conf, extensions.conf are in attachment. In the same

Hi, I am working with gam models in the mgcv library. My response variable (Y) is binary (0/1), and my dataset contains repeated measures over 110 individuals (same number of 0/1 within a given individual: e.g. 345-zero and 345-one for individual A, 226-zero and 226-one for individual B, etc.). The variable Factor is separating the individuals in three groups according to mass (group 0,1,2),

Dear list, I have another (again possibly boneheaded) puzzle about importing, again encapsulated in a nearly trivial package. (The package is posted at <http://www.math.mcmaster.ca/bolker/misc/coefsumtest_0.001.tar.gz>.) The package consists (only) of the following S3 method definitions: coeftab <- function(object, ...) UseMethod("coeftab",object) coeftab.default <-

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

I am currently running a generalized linear mixed effect model using glmer and I want to estimate how much of the variance is explained by my random factor. summary(glmer(cbind(female,male)~date+(1|dam),family=binomial,data= liz3")) Generalized linear mixed model fit by the Laplace approximation Formula: cbind(female, male) ~ date + (1 | dam) Data: liz3 AIC BIC logLik deviance 241.3

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120",

Hello all and guest@misery.digium.com , I was surprised to play the "demo" extension from my Asterisk CVS. It was around "Fri May 23 22:18:37 CEST 2003". While trying to make a test with chan_h323, I got a "wrong" number and fell down on someone at this address IAX2/guest@misery.digium.com/s@default. The voice was clear but unfortunately I don't understand

Hi All, I have a microarray dataset as follows:                           expt1 expt2 expt3  expt4 expt 5  gene1                val      val     val      val     val gene2                val      val     val      val    val . . .. gene15000       val       val     val      val     val The result is from the same organism in four different experiments.  Also, there are 4 replicates of each

I am currently attempting to hack the recently featured profileModels package so that it can handle models generated by the lme4 (mixed models) package. I'm getting really confused by different behavior of summary() before and after loading the lme4 package, and inside and outside the profileMethod() function. The basic behavior is that with lme4 loaded, and "obj" a fitted object

Hi, I'm working with gamm models of this sort: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group if I use plot(gm1$gam), I obtain 3 different surface plots, one for each level of my factor but I would like to create more complex contour plots for those 3