search for: logmu

Dear all, I would like to ask, if there is a way to make the variance / dispersion parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate the parameter vector $\beta$ additionally. If this is not possible with glm.nb, is there another function / package which might do that? Thank you very much for

Hi, I am trying to run a generalized linear regression using a negative binomial error distribution. However, I want to use an overdispersion parameter that varies (dependent on the length of a stretch of road) so glm.nb will not do. >From what I've read I should be able to do this using GLM by specifying my own quasi family and describing the variance function using varfun, validmu,

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

Dear R community, I would like to compare the degree of aggregation (or dispersion) of bacteria isolated from plant material. My data are discrete counts from leaf washes. While I do have xy coordinates for each plant, it is aggregation in the sense of the concentration of bacteria in high density patches that I am interested in. My attempt to analyze this was to fit negative binomial

...- astar.1 > astar[n] <- astar.1 > A <- astar/sqrt(sum(astar^2)) > W <- (sum(A * y)^2)/SSq > if(n <= 20) { > u <- log(n) - 3 > lambda <- 0.118898 + 0.133414 * u + 0.327907 * u^2 > logmu <- -0.37542 - 0.492145 * u - 1.124332 * u^2 - > 0.199422 * > u^3 > logsigma <- -3.15805 + 0.729399 * u + 3.01855 * u^2 + > 1.558776 > * > u^3 > } > if(n > 20) { > u &l...