search for: negbin

I have tried glm.nb in the MASS package, but many models (I have 250 models with different combinations of predictors for fish counts data) either fail to converge or even diverge. I'm attempting to use the negbin function in the AOD package, but am unsure what to use for the "random" term, which is supposed to provide a right hand formula for the overdispersion parameter. I'm not even sure what this statement means. Any advice you have would be greatly appreciated. negbin(formula, random,...

Hi, When modeling with negbin from the aod package, parameters for a given count y | lambda~Poisson(lambda) with lambda following a Gamma distribution Gamma(r, theta) are estimated. The intercept is called phi. Some other parameters may be also be estimated from factors in the data: the estimates returned for all these would...

I have fitted the faults.data to glm.nb and to the function negbin from the package aod. The output of both is the following: summary(glm.nb(n~ll, data=faults)) Call: glm.nb(formula = n ~ ll, data = faults, init.theta = 8.667407437, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.0470 -0.7815 -0.1723 0.4275 2.0896...

...i.e. how well does chick size predict tape intensity? Since I have a small sample size, I fit a zero inflated negat. Binomial (not Poisson) model using the "pscl" package. I built tried two models and got the outputs below. > model <- zeroinfl(Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE) Call: zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE) Count model coefficients (negbin with log link): (Intercept) CAPI -2.99182 0.06817 Theta = 0.4528 Zero-inflation model coefficients (binomial with logit link): (Intercept)...

Hi, I wonder if predict.gam is supposed to work with family=negbin() definition? It seems to me that the values returned by type="response" are far off the observed values. Here is an example output from the negbin examples: > set.seed(3) > n<-400 > dat<-gamSim(1,n=n) > g<-exp(dat$f/5) > dat$y<-rnbinom(g,size=3,mu=g)...

Dear R people, I am not sure if this is the correct place to tell about problems in evolving programmes, but it seems that the `summary.negbin' function of the excellent `MASS' library is now broken, and gives the following error message: > summary(hm) Error in summary.negbin(hm) : subscript out of bounds `summary.negbin' calls `summary.glm' which seems to work and give the desired output like called there, but call...

Dear Clara, Thanks for the reply. I am forwarding your message to the list, ok. When I wrote was a way of get further information to help the helpers. happy holidays, milton ---------- Forwarded message ---------- From: Clara Brück <clara_brueck@web.de> Date: 2009/12/30 Subject: Re: [R] Negbin Error Warnings To: milton ruser <milton.ruser@gmail.com> Dear Milton, Thanks for your email. I ran the NBR-model and then tried to compute the first differences of some expected values: m3.x1 <-setx(m3, advisory=1) m3.x2 <-setx(m3, advisory=0) m3.s1 <-sim (m3,x=m3.x1, x1=m3.x2)...

Dear R-collegues, I'm getting an error message (Error in round) when summarising a glm.nb model, and when using anova.negbin (in R 1.3.1 for windows): > m.nb <- glm.nb(tax ~ areal) > m.bn Call: glm.nb(formula = tax ~ areal, init.theta = 5.08829537115498, link = log) Coefficients: (Intercept) areal 3.03146 0.03182 Degrees of Freedom: 283 Total (i.e. Null); 282 Residual Null Deviance: 353...

...rsion parameter from a (Poisson) gamm? I have not been able to extract residual degrees of freedom from M1. 2) How to manually estimate theta for a negative binomial gamm? I would like to see if applying a negative binomial distribution with log link (model below) would solve the problem. However, negbin in gamm requires a known theta... M2 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family= negbin(THETA, link="log")) 3) And finally, can I somehow compare the mode...

...g at the association between these two variables, i.e. how well does chick size predict tape intensity? I fit a zero inflated negat. binomial model using the "pscl" package. I built my model as follows and got the output below. > model <- zeroinfl(Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE) > model Call: zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE) Count model coefficients (negbin with log link): (Intercept) CAPI -2.99182 0.06817 Theta = 0.4528 Zero-inflation model coefficients (binomial with logit link): (Inte...

Hi, I ran a negative binomial regression (NBR) using the Zelig-package and the negbin model. When I then try to use the simumlation approach using the setx () and sim() functions to calculate expected values and first difference for different levels of one of my independent variables, I get 50 errors warnings, telling me that the calculation rpois produced NAs. However, the data I...

...I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me. I was previously using this script for my overdispersed gam data: M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus) My gam.check gave me the attached result. In order to deal with my heterogeneity, I need to switch over to a gamm structure and use at least one, but possibly multiple, variance structures, and I am starting by applying varPower to my temperature covariate. (Efuscus i...

Hi there, I'm using presence-absence data in a gam (i.e. 0 or 1 as values) I am trying to run a gam with 'dummy covariates' i.e. 1~1 unfortunately my model: * model<-gam(1~1, data=bats, family=negbin)* keeps putting out: * Error in gam(1 ~ 1, data = bats, family = negbin) : Not enough (non-NA) data to do anything meaningful* Is there a specific reason it would do this? I have tried using various actions on NAs, na.pass etc, as well as asking it to turn all NAs to 0 but to no avail. I wou...

Edward, you also can use the package aod on CRAN, see the help page of the function negbin. Best Matthieu An example: > library(aod) > data(dja) > negbin(y ~ group + offset(log(trisk)), ~group, dja, fixpar = list(4, 0)) Negative-binomial model ----------------------- negbin(formula = y ~ group + offset(log(trisk)), random = ~group, data = dja, fixpar = list(4, 0))...

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

...n R. I have to deal with zero inflated negative binomial distribution. I am however unable to understand the following example from this link http://www.ats.ucla.edu/stat/r/dae/zinbreg.htm The result gives two blocks. *library(pscl) zinb<-zeroinfl(count ~ child + camper | persons, dist = "negbin", EM = TRUE) summary(zinb) *Call: zeroinfl(formula = count ~ child + camper | persons, dist = "negbin", EM = TRUE) Count model coefficients (negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 1.3711 0.2561 5.353 8.63e-08 *** child...

Folks, Thanks for the help with the hier.part analysis. All the problems stemmed from an import problem which was solved with file.chose(). Now that I have the variables that I'd like to use I need to run some GLM models. I think I have that part under control but I'd like to use a jackknife approach to model validation (I was using a hold out sample but this seems to have fallen out

...on-count-vs-response ). Since the issue is the same (and not resolved), I'll just use the example from the second link: library("pscl") data("RecreationDemand", package = "AER") ## model m <- hurdle(trips ~ quality | ski, data = RecreationDemand, dist = "negbin") nd <- data.frame(quality = 0:5, ski = "no") predict(m, newdata = nd, type = "count") predict(m, newdata = nd, type = "response") The presence/absence part of the model gives identical estimates to a logistic model run on the data. However, I thought that the...

...- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn = loglikfun, gr = gradfun, par = c(start$count, start$zero, : non-finite value supplied by optim For what I have read I think the problem is that for Year level 3 there is no Trt 1 and for Year level 1 there is no Micro level 3. I cannot find a solution to this problem,...

...often seems only to change in the second or third decimal place for the same models. Is there a standard for how many decimals one should look at GCV, or am I totally missing something (I'm quite new to this, as I'm sure is obvious). Why such a difference in GCV change between poisson and negbin? Thanks in advance! Jordan library(mgcv) set.seed(0) n<-400 sig<-2 x0 <- runif(n, 0, 1) x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <- runif(n, 0, 1) f0 <- function(x) 2 * sin(pi * x) f1 <- function(x) exp(2 * x) f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1...