similar to: glm.nb - theta, dispersion, and errors

Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

Dear R users I would like to simulate underdispersed Poisson and binomial distributions somehow. I know you can do this for overdispersed counterparts - using rnbinom() for Poisson and rbetabinom() for binomial. Could anyone share functions to do this? Or please share some tips for modifying existing functions to achieve this. Thank you very much for your help and time Shinichi

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit

Using anova of a glm with test = "Chisq", I get this: Analysis of Deviance Table Model: poisson, link: log Response: Days Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL 373 370.56 Block 3 71.05 370 299.51 2.543e-15 Variety 1 94.04 369

Hello R-help   I am working with large data table that have the occasional label,  a particular time point in an experiment. E.g: "Time (min)", "R1 R1", "R2 R1", "R3 R1", "R4 R1" .909, 1.117, 1.225, 1.048, 1.258 3.942, 1.113, 1.230, 1.049, 1.262 3.976, 1.105, 1.226, 1.051, 1.259 4.009, 1.114, 1.231, 1.053, 1.259 4.042, 1.107, 1.230, 1.048, 1.262

Hello, Anyone who knows a fast and accurate algorithm for generating draws from an underdispersed Poisson distribution. Or even better, if there is a package containing such an implementation. Thanks Rene

I'm having problems (or to be precise a student is having problems, which I'm having problems helping her with) trying to use glm.nb() from the MASS package to do some negative binomial fits on a data set that is, admittedly, wildly overdispersed (some zeros and some numbers in the hundreds). glm.nb is failing to converge, and furthermore is (to my surprise) producing values of theta

I came to R from GLIM and its glm. My data sets (ecological community data) are severely over-dispersed, and so I was delighted to find out that the MASS library has glm.nb which is an advancement from the GLIM macros I had used (N.E.Breslow, Applied Statistics 33, 38--44; 1984). However, I need to use offset, but that failed. I am not (yet --- hopefully) fluent enough in R to be able to

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

Hi, I am a bit stuck on specifying ui and ci. I have read Lange's book ((1999) Numerical Analysis for Statisticians) to his approach and unfortunately his descriptions were not helpful for me. Here is what I have: ui <- rbind(c(0, -1, 0), c(0, 0, -1)) ci <- c(0, -1, -1)) theta <- c(0.5, 0.5, 0.1) My goal is to feed these into constrOptim

Hello, I am working on the frailty model using coxph functions. I am running some simulations and want to store the variance of frailty (theta) values from each simulation result. Can anyone help me how to extract the theta values from the results. I appreciate any help. Thanks Shankar Viswanathan

I am fitting a negative binomial regression model to some count data. I chose the negative binomial b/c the variance is greater than the mean. Anyways, when I fit the model I get the following warning: "Warning while fitting theta: alternation limit reached" The estimate that I end up with is very large (1070), and the standard error is even larger (1276). Does this indicate that I

Dear all, Does the frailty.object$history[[1]]$theta returns the Variance of random effect? Why is the value different? Here is an example with kidney data: > library(survival) > data(kidney) > frailty.object<-coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney) > frailty.object Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id), data

Dears, Oracle 8 don''t support ANSI syntax with : SELECT e.emp_id, e.fname, e.lname, j.jobdesc FROM employe e LEFT JOIN jobs j ON e.job_id = j.job_id but only SELECT e.emp_id, e.fname, e.lname, j.jobdesc FROM employe e, jobs j WHERE j.job_id (+) = e.job_id JOIN syntax came with 9i. Anyone patched Rails

Dear All, Consider the following code: plot(0,0) text(0,0.5,expression(italic(theta))) I would like to get theta in italic, but I always get it upright. Any suggestions? Thanks in advance, Paul

I am using package LTM to estimate a Rasch model: irtestimates <- rasch(binRasch) I want to get a single vector containing theta estimates for all the rows (individuals) in my data matrix (hopefully in the same order as my data matrix) such that the length of the theta vector = the number of rows (participants) in my data matrix. I am using: theta.est <-

Dear R users, I am trying to use "gamm" from package "mgcv" to model results from a mesocosm experiment. My model is of type M1 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family=poisson(link=log)) where the response variable is counts, offset by the

Hi all, what indicates the parameter theta in the summary of a random effect panel model estimated with the plm function? example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) summary(zz) Effects: var std.dev