search for: nbinom

...e[2]: Leaving directory `/mnt/linuxApp/usr/local/R/R-patched/tests' make[1]: *** [test-all-devel] Error 1 make[1]: Leaving directory `/mnt/linuxApp/usr/local/R/R-patched/tests' make: *** [check-all] Error 2 " The tail of ./tests/p-r-random-tests.Rout.fail is: " > dkwtest("nbinom",size = 1,prob = 0.8) nbinom(size = 1, prob = 0.8) PASSED [1] TRUE > dkwtest("nbinom",size = 100,prob = 0.8) nbinom(size = 100, prob = 0.8) PASSED [1] TRUE > dkwtest("nbinom",size = 100,prob = 0.999) nbinom(size = 100, prob = 0.999) PASSED [1] TRUE > > dkwtest...

...ois(lambda = 0.095) PASSED [1] TRUE > dkwtest("pois",lambda = 0.95) pois(lambda = 0.95) PASSED [1] TRUE > dkwtest("pois",lambda = 9.5) pois(lambda = 9.5) PASSED [1] TRUE > dkwtest("pois",lambda = 95) pois(lambda = 95) PASSED [1] TRUE > > dkwtest("nbinom",size = 1,prob = 0.2) nbinom(size = 1, prob = 0.2) PASSED [1] TRUE > dkwtest("nbinom",size = 2,prob = 0.2) nbinom(size = 2, prob = 0.2) PASSED [1] TRUE > dkwtest("nbinom",size = 100,prob = 0.2) nbinom(size = 100, prob = 0.2) PASSED [1] TRUE > dkwtest("nbin...

Hello, I was wondering if weighting has been added to glmmadmb as of yet? I found a post about a year ago that it it hadn't been implemented yet but I was hoping the documentation may just have not yet caught up. Thanks, Trevor Davies [[alternative HTML version deleted]]

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

...land) first I removed na's from my data callsna <- na.omit(calls) Then I started simple, with only "moonlight" and "weather" included in a model (just as a test): mod <- glmmadmb(Calls~ Moon+Wind.Speed+(1|SITE/ISLAND), data=callsna, zeroInflation=TRUE, family="nbinom") I get the following error message: Error in II[, ii] = II[, ii] + REmat$codes[[i]] : replacement has length zero In addition: Warning message: In matrix(rep(q, m), nrow = n, ncol = sum(m), byrow = TRUE) : data length exceeds size of matrix I get the same error message when I try with...

...name of the grouping variable (also when "random" is missing) Here is what I have tried so far (along with some similar variations): model_nb<-glmm.admb(data$total_bites_rounded~age_class_back+(1|"subject")+(1|food.dif.id)+offset(log(forage_time)),data=data,family="nbinom") modelnb<-glmm.admb(data$total_bites_rounded~age_class_back, random=~food.dif.id, group="subject", data=data, offset=offset,family="nbinom") I am not sure what I am doing wrong. My model in lmer that seemed to work was: modelc<lmer(data$total_bites_rounded~age_c...

Hello everybody, I am using glmmADMB and when I run some models, I recieve the following message: Erro em glmmadmb(eumencells ~ 1 + (1 | owners), data = pred3, family = "nbinom", : The function maximizer failed (couldn't find STD file) Furthermore: Lost warning messages: Command execution 'C:\Windows\system32\cmd.exe /c "C:/Users/helenametal/Documents/R/win-library/2.15/glmmADMB/bin/windows32/glmmadmb.exe" -maxfn 500 -maxph 5 -noinit -shess' te...

library(glmmADMB) #Example for glmm.admb data(epil2) glmm.admb(y~Base*trt+Age +Visit,random=~Visit,group="subject",data=epil2,family="nbinom") Gives: Error in glmm.admb(y ~ Base * trt + Age + Visit, random = ~Visit, group = "subject", : The function maximizer failed ****************** R version 2.4.1 RC (2006-12-14 r40181) powerpc-apple-darwin8.8.0 locale: C attached base packages: [1] "utils" "...

...ive binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or overdispersed poisson). Is there any other package that offers this functionality? Thanking you in advance, Costas [1] Yes, I know I can use this on another OS. But it is kind of a nuisance, as I have my whole workflow setup on a mac, including emacs +ess, the data etc etc. It will be...

...ata set > ?stain.csv?. I have also attached a script file with all of the code > that should (hopefully) work for you. > > What I would like to get working is the function: > > nbin1<-glmmadmb(stainp~beetle.ev+Caged*Section/Segment+(1|Site)+(1|Log.code),data=dat1,family="nbinom") > > The issue is with the ?beetle.ev? column (binary, presence/absence of > insects). Below is some code that works, all that has changed is that > ?beetle.ev? is substituted with ?Test?. This one works, and has some > data in pretty much the same format as ?beetle.ev? (as yo...

Hello, I like to fit data against a negative binominal distribution x2<-c(rep(10,14),rep(9,8),rep(8,13),rep(7,11),rep(6,6),rep(5,18),rep(4,7),re p(3,21),rep(2,33),rep(1,55),rep(0,225)) f2<-fitdist(x2,"nbinom",method="mle") plot(f2) summary(f2) gofstat(f2) I receive the following result: Fitting of the distribution ' nbinom ' by maximum likelihood Parameters : estimate Std. Error size 0.3397148 0.03775577 mu 1.7787630 0.16429774 Loglikelihood: -701.0615...

...d land class (1 to 6). Ideally I would also like to build in a variance structure to allow a different spread per land class. This is the model I'm trying to run: (fm<-glmmadmb(count~species*btrees+species*built+species*btrees*built+(1|season)+(1|landclass)+(1|site), data=srp12, famil="nbinom", zeroInflation=TRUE)) I have read most of the supporting documents to glmmADMB and studied the examples but am still struggling to make headway. This is the error message I get; Memory allocation error -- Perhaps you are trying to allocate too much memory in your program Warning: running...

...ning R version 2.15.0 and started out using the most recent version of glmmADMB (I believe version 7.2.15). My data is zero inflated count data that is overdispersed. When I ran the following code for either a zero inflated poisson (family="poisson") or a neg binomial (i.e., family="nbinom"): fit_zipoiss1 <- glmmadmb(LOCS~D_ROADS + (1|YEAR) , data=FAWNS, zeroInflation=TRUE, family="poisson", mcmc=TRUE) I get the error: Error in glmmadmb(LOCS ~ D_ROADS + (1 | YEAR), data = FAWNS, zeroInflation = TRUE, : The function maximizer failed (couldn't find STD file)...

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan

...glmm.admb (from glmmADMB pack)     My first problem: yesterday this syntax was ok, now I get this weird message (I got it before when I was using my own set of data)   >        data(epil2) >        glmm.admb(y~Base*trt+Age+Visit,random=~Visit,group="subject",data=epil2,family="nbinom") 'C:/Documents' is not recognized as an internal or external command, operable program or batch file. Error in glmm.admb(y ~ Base * trt + Age + Visit, random = ~Visit, group = "subject",  :   The function maximizer failed In addition: Warning messages: 1: In file.remove(std...

[code]library(MASS) x=c(rep(0,8096), rep(1,1629), rep(2,233), rep(3,38), rep(4,4) ) x.bar=round(mean(x),4) x.var=round(var(x),4) p.hat=round(x.bar/x.var,4) alpha.hat=round(x.bar*p.hat/(1-p.hat),4) fitdistr(x, "Negative Binomial") fitdistr(x, "Poisson")[/code] 1- fitdistr(x, "Negative Binomial") the parameters got here,

...e easy enough to test and return NA under these circumstances? function (x, size, prob, mu, log = FALSE) { ~ if (!missing(mu)) { ~ if (!missing(prob)) ~ stop("'prob' and 'mu' both specified") ~ prob <- size/(size + mu) ~ } ~ .Internal(dnbinom(x, size, prob, log)) } ~ Ben Bolker - -------------------------- ~ Check it out: curve(dnbinom(1,mu=0.5,size=x),log="x",from=1,to=1e18) abline(h=dpois(1,lambda=0.5),col=2,lty=2) text(1,dpois(1,lambda=0.5)+0.02,"Poisson",col=2,pos=4) ~ I will take a look in the C code whe...

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

...rent to each approach. Following the logic of D. Bates (R-sig-mixed models list serv: 13 Oct 08), I decided to pursue the LLRT approach. Still, I have several questions. If this is my model: m <- glmm.admb(fixed = y ~ b1 + b2 + I(b2^2) + b1:b2, random = ~ 1, group = ?subject?, family = ?nbinom?, data = dataset) and b1 = a categorical variable with 3 levels b2 = a continuous variable and I?ve set my contrasts as follows: options(contrasts =c("contr.treatment", "contr.poly")) dataset$b1 <- factor (as.character(dataset$b1), levels=c("L1", "L2",...

...the (possibly incorrect) 'NO FILE'. o window() did not allow non-overlapping ranges with extend = TRUE. (PR#8545) o pbinom(size = 0) now returns correct values (not NaN). (PR#8560) o [dp]binom(x, *) for x < 0 now always returns 0. (PR#8700) analogous change in pgeom(), pnbinom() and ppois(). o [dqpr]geom and [dpqr]nbinom() now all consistently accept prob = 1 but not prob = 0. qgeom(prob=1) now gives the correct values (not -1). o INSTALL on Unix-alikes was not loading dependent packages when preparing for lazy-loading. o qcauchy(1) now gives +Inf inst...