similar to: [multi-level model] set up categorical variable/indicator?

I'm new to bugs, so please bear with me. Can someone tell me if the following two models are doing the same thing? The reason I ask is that with the same data, the first (based on 4 separate coeffs a1--a4) takes about 50 secs, while the second (based on a vectorized form, a[]) takes about 300. The means are about the same, though R-hat's in the second version are quite a bit better.

Dear friends - I'm on winXP, R 2.71 - I have with some help dveloped this multivariate normal model, which gives very plausible results in WinBUGS even without any initial values specified. However, when I then try to run the same model via the bugs function in R2WinBUGS with inits specified as inits=NULL the program stops in a dead end. So I have tried to make inits for the bugs function

Hello, I am running a GLMM using R2Bugs, but am getting the below error message. I am including the entire output, although the 2nd and 3rd lines seem to indicate the problem. Note that I do define N (it is an integer) and send it to a datalist (see the R commands and model below). Interestingly, when I put all of this into OpenBugs directly, the model runs. I do have reasons, however, that I

Hi R users, I am trying to call openbugs from R. And I got the following error message: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ model is syntactically correct expected the collection operator c error pos 8 (error on line 1) variable ww is not defined in model or in data set [1] "C:\\DOCUME~1\\maomao\\LOCALS~1\\Temp\\RtmpqJk9R3/inits1.txt"

Hi, I am trying to use dunif and runif however, I have two problems: if I do dunif(1:10, min=1, max=10) I get 10 values, which summed give me 1.1111 I understand that the probability is computed as f(x) = 1 / (max-min) but in this case it looks wrong: I have 10 values, each one equiprobable, and the probability for each one should be 0.1 and not 0.11111 (which is, consistently with the

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)

Hi, I have a strange behaviour of openBUGS and WinBUGS when I start them from R. Version: R: 2.10.1 openBUGS: 3.07 WinBUGS: 1.43 R2WinBUGS: 2.1-16 BRugs: 0.5-3 I have a model and data without initial start values. If I use the stand alone versions of openBUGS and WinBUGS I don't have any problems and I get what I want. If I use function bugs() from R2WinBUGS- resp. BRugs-Library then

Hi, I am trying to do run the following model saved in "C:/bugs/sus.bug" model { for (i in 1:n){ y[i] ~ dpois(lamdba[i]) log(lambda[i]) <- mu+bmale[male[i]]+bschn[schn[i]]+epsilon[i] # epsilon[i] ~ dnorm(0,tau.epsilon) } mu ~ dnorm(0,.0001) bmale ~ dnorm(0,.0001) tau.epsilon <- pow(sigma.epsilon, -2) sigma.epsilon ~ dunif(0,100) for (j in

Hi, I'm currently working on the below codes however whenever I run it in openbugs it gives an error message saying: unknown type of logical function error pos 76. Any help would be appreciated. ## bugs code library(R2OpenBUGS) sink("C:/Users/CCF/Documents/Suzie Work/PTY Project/Waterhole Correction/ungulate.txt") cat(" model{ # hyperparameters # habitat effects for each

Hi, I'm working on the codes below however every time I run them when they get to OpenBUGS I keep getting the error message: array index is greater than array upper bound for hab. Any help would be greatly appreciated, Suzie Codes: ungulate <- read.csv(file.choose ()) #ungulate ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL

Hi there, I'm trying to find the KL divergence measure between a prior and it's posterior distributions, and I'm using the KLdiv method in the flexmix package. plese see the example below: require(flexmix) x=seq(-4,4,length=100) d1=dnorm(x,0,1) d2=dunif(x,-3,3) y=cbind(d1,d2) kl=KLdiv(y) but let say, x1=seq(-5,5,length=100) d3=dunif(x1,-3,3) y1=cbind(d1,d3) kl1=KLdiv(y1) Notice

Hi List, I had the pleasure of taking Dr Bob Muenchen's interview for his upcoming book R For SAS and SPSS users. He has spent 27 years in this field while I have spent almost that much on earth. So this is more like a fan blog interview. I thought it would be of use to people curious about R, or even SAS , or SPSS if they have not worked on either of these packages before. Having fought my

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

Dear Lists, I got the chance to review Bob Muenchen's book - R for SAS and SPSS users. While my detailed review is online at www.decisionstats.com, the book is a welcome addition in R Textbooks for beginners and an especially nice one for people who have experience in SAS and /or SPSS languages. AT 50 USD, it's reasonably priced for all geographies (;) , and quite handy as a refernce book

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Hi, Bob Muenchen's book is now out on release. Its terrific for a step by step tutorial for doing stuff in R, even if all you know is SAS and SPSS. Check this out here http://rforsasandspssusers.com/ and at http://www.amazon.com/SAS-SPSS-Users-Statistics-Computing/dp/0387094172/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1217456813&sr=8-1 Regards, Ajay www.decisionstats.com

Hi. I am new here, and I am writing this Winbugs code with BRugs. n=length(bi.bmi) Lagegp=13 Lgen=2 Lrace=5 Lstra=15 Lpsu=2 #model gen x race bi.bmi.model=function(){ # likelihood for (i in 1:n){ bi.bmi[i]~ dbern(p[i]) logit(p[i])<- a0 + a1[agegp[i]]+a2[gen[i]]+a3[race[i]] + a12[agegp[i], gen[i]] + gam[stra[i]]+ u[psu[i],stra[i]] } # constraints for a1, a2, a3, a12 a1[1]<-0.0

Hi all, Apologies in advance if this is really too simple, but I have given a good shot at this. I'm generating a standard uniform distibution y <- dunif(x) plot(x,y,type="l") This will draw a horizontal line at y = 1. I want to fill the area from x=(0,1) with a color. I've tried the polygon command to create a polygon of the same shape, but can't seem to set the

All I have been looking into the books on performing statistics in R, in particular I am interested in General Linear Mixed Models, for Randomized Complete Block Design Experiments The list I have come away with so far is: Mixed Effects Models in S and S-plus by Pinheiro (2002)