similar to: Negative binomial regression for count data,

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Hi all, I have made an overdispersion test for a data set and get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.24267 47444.85 0 after deleting the outliers from the data set I get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.27106 0 1 The

Hi, there are a lot of messages dealing with overdispersion, but I couldn't find anything about how to test for overdispersion. I applied a GAM with binomial distribution on my presence/absence data, and would like to check for overdispersion. Does anyone know the command? Many thanks, Anna -- View this message in context:

Dear R users, I am trying to fully understand the difference between estimating overdispersion with glm.nb() from MASS compared to glm(..., family = quasipoisson). It seems that (i) the coefficient estimates are different and also (ii) the summary() method for glm.nb suggests that overdispersion is taken to be one: "Dispersion parameter for Negative Binomial(0.9695) family taken to be

Hello, The share of concurring votes (i.e. yes-yes and no-no) in total votes between a pair of voters is a function of their ideological distance (index continuous on [1,2]). I show by other means that the votes typically are highly positively correlated (with an average c=0.6). This is because voters sit together and discuss the issue before taking a vote, but also because they share common

Dear R users, I work with a descrete variable (sclae 0 - 27) which is highly skwed to the right (many zeros and small numbers). I measure this variable on a control and intervention cohort 5 times a year. When I analyze analyze this varoable at each time point separately and use GLM with family quasi-Poisson (descrete outcome and two binary variables, gender and cohort, are predictors), I observe

Dear list members, while I appreciate the possibility to deal with overdispersion for count data either by specifying the family argument to be quasipoisson() or negative.binomial(), it estimates just one overdispersion parameter for the entire data set. In my applications I often would like the estimate for overdispersion to depend on the covariates in the same manner as the mean. For example,

I am looking for the correct commands to do the following things: 1. I have a binomial logistic regression model and i want to test for overdispersion. 2. If I do indeed have overdispersion i need to then run a quasi-binomial model, but I'm not sure of the command. 3. I can get the residuals of the model, but i need to then apply a shapiro wilk test to test them. Does anyone know the command

Hello everybody, I have count data and with these data, I would like to build a mixed model by using the function glmer(). In a first time, I calculated the c-hat of a simple model with glm() to verify overdispersion and I found a c-hat = 18. I also verified overdispersion in the mixed model by checking the residuals of random effects via the function glmmPQL and I found a c-hat = 15. Thus,

Hello, I'm a grad student in the Intelligent Transportation Systems lab at Portland State Univ. in Portland, OR, USA. I'm trying to learn the basics of R to run a negative binomial in the near future, and so I ran a test regression on roadway crash data obtained from "Statistical and Econometric Methods for Transportation Data Analysis" by Washington et al (p. 250). I ran the

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented

Dear friends, As we all know, the usual model selection criteria(e.g.deviance,AIC...) in GLMs isn't very good for selecting the best model when overdispersion exist, so we need to adjust the corresponding statistic,see(Fitzmaurice,G.M. (1997) Model selection with overdispersed

Hello, I am analysing counts data with a mixed model using lmer procedure. I therefore use the quasipoisson distribution but I'm not sure if this is sufficient to account for overdispersion. Actually the results are not very different to what I get when specifying a poisson distribution although my data are clearly overdispersed. this my model: >model <- lmer(NB ~ T + volume +

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hola! I have a problem which is not strictly R, although R will be used for the analysis. We have data from a large investigation of drug abuse, initially analyzed by logistic regression. But the pupils are selected by first sampling schools, and as it happens the prevalence of use varies sharply from school to school, so there is over-dispersion. Now we are interested in comparing the

Hey Buddy, Hope you have been doing well since last contact. If you have the answer to the following question, please let me know. If you have chance to travel up north. let me know. best, -Sean ---------- Forwarded message ---------- From: Sean Zhang <seanecon@gmail.com> Date: Sat, Apr 11, 2009 at 12:12 PM Subject: question related to fitting overdispersion count data using lmer

Dear All I recently proposed a simple modification to Wedderburn's 1974 estimate of overdispersion for count and binomial data, which is used in glm for the quasipoisson and quasibinomial families (see the reference below). Although my motivation for the modification arose from considering sparse data, it will be almost identical to Wedderburn's estimate when the data are not sparse.

I have a few questions relating to overdispersion in a sex ratio data set that I am working with (note that I already have an analysis with GLMMs for fixed effects, this is just to estimate dispersion). The response variable is binomial because nestlings can only be male or female. I have samples of 1-5 nestlings from each nest (individuals within a nest are not independent, so the response