search for: gm2

Hi all, I try to ask helps again about chan_h323 extensions. I define this in h323.conf: [general] port = 1720 bindaddr = 0.0.0.0 allow=gsm allow=ulaw gatekeeper = DISABLE context=default [gm1] type=friend host=192.168.1.20 context=default [gm2] type=friend host=192.168.1.25 context=default and I have in extensions.conf : [demo] ... [default] include => demo exten => 1020,1,Dial(H323/gm1@192.168.1.20) exten => 1025,1,Dial(H323/gm2@192.168.1.25) Then from a H.323 client 192.168.1.25 I try...

...s instead of tensor product smooths. In this case, the person was comparing between model A and model A1 like: A<-gamm4(Z~factor+s(x)+s(x,by=factor) ,data=DATA, random=~(1|Group)) A1<- gamm4(Z~factor+s(x) ,data=DATA, random=~(1|Group)) I thus also tried to compare my model gm1, with another gm2 model of that sort: gm2<-gamm(Z~te(x,y)+te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) but this type of models never converge and I obtain error messages of that sort: Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1 2 questions from that: 1) Keeping...

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 same host of a H.323 client (GnomeMeeting). When I tried to make a call from GnomeMeeting like this: c...

...p-value < 0.1, I get a model with 4 variables and 2 interactions and an AIC of 33.879 So my questions: Why didn't the stepAIC give me the model with AIC=33.879? And which model should I think of as the best? For my calculations I used these formulae: gm1<-glm(cpi~time+tank+...,data=d1) gm2<-stepAIC(gm1) summary(gm2) #to get the "best" model -> AIC=40.008 #afterwards I reduced manually using the formula: summary(glm(cpi~time+tank+...,data=d1)) giving me a model with AIC=33.879 Hope you understand what I did, and that you can help me. Thanks Florian [[alternative...

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 all 36 to compare together but I can't even get a plot to have the GM1-GM12...

...ind(id,group)) a id group 1 1 1 2 2 1 3 3 1 4 4 1 5 5 2 6 6 2 7 7 3 8 8 3 9 9 3 10 10 3 11 11 3 12 12 3 Result should be (gm = groupmember) id group gm1 gm2 gm3 gm4 gm5 gm6 gm7 etc. 1 1 2 3 4 NA NA NA NA 2 1 1 3 4 NA NA NA NA 3 1 1 2...

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 p-value < 0.1, I get a model with 4 variables and 2 interactions and an AIC of 33.879 So my questions: Why didn't the stepAIC give me the model with AIC=33.879? And which model should I think of as the best? For my calculations I used these formulae: gm1<-glm(cpi~time+tank+...,data=d1) gm2<-stepAIC(gm1) summary(gm2) #to get the "best" model -> AIC=40.008 #afterwards I reduced manually using the formula: summary(glm(cpi~time+tank+...,data=d1)) giving me a model with AIC=33.879 Hope you understand what I did, and that you can help me. Thanks Florian [[alter...

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,

...837 period4 -1.9450 0.4615 -4.215 Correlation of Fixed Effects: (Intr) perid2 perid3 period2 -0.339 period3 -0.314 0.219 period4 -0.233 0.163 0.151 ############# now the commands for the quasibinomial: library(lme4) toto<-as.double(cbpp$incidence>0) gm2 <- lmer(toto ~ period + (1 | herd), family = quasibinomial, data = cbpp) summary(gm2) ########## here is the result under R2.5.1 and Package lme4 version 0.99875-9: Generalized linear mixed model fit using Laplace Formula: toto ~ period + (1 | herd) Data: cbpp Family: quasibinomial(log...