similar to: Negative Binomial modelling

I am having trouble with optim. It claims to have converged to a minimum, yet it has in the course of the optimization visited many points which are closer to optimal. I would be grateful for any explanation of this behaviour. I'm trying to estimate the parameters in the model X ~ Binomial(1,p) * NegBin(mu,theta). So I define a log likelihood function, and invoke optim thus: o <- optim

Does anyone have R functions or library to fit a beta-binomial distribution with glm? Thanks.

Hello, I am interested in fitting a generalized nonlinear regression (gnlr) model with negative binomial errors. I have found Jim Lindsay's package that will do gnlr, but I have having trouble with the particular model I am interested in fitting. It is a threshhold model, where below a certain value of one of the parameters being fitted, the model changes. Here is a sample: Cones:

Hi R-Users! I fitted a negative binomial distribution to my count data using the function glm.nb() and obtained the calculated parameters theta (dispersion) and mu. I would like to simulate values from this negative binomial distribution. Looking at the function rnbinom() I was looking at the relationship between the two possible parametrizations of the negative binomial and found that for this

Why does > curve(function(y) y^2, from=0,to=1) not work, whereas > myf <- function(y) y^2 > curve(myf, from=0,to=1) work? For the former, I get the error message Error in curve(function(y) y^2, from = 0, to = 1) : 'expr' must be a function or an expression containing 'x' I'm using R1.7.0 under Windows XP. Damon Wischik.

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

Dear R users, I'm wanting to sample from the negative binomial distribution using the rnegbin function from the MASS library to create artificial samples for the purpose of doing some power calculations. However, I would like to work with samples that come from a negative binomial distribution that includes only values greater than or equal to 1 (a truncated negative binomial), and I

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

Dear All, I am trying to fit some data both as a negative binomial and a zero inflated binomial. For the first case, I have no particular problems, see the small snippet below library(MASS) #a basic R library set.seed(123) #to have reproducible results x4 <- rnegbin(500, mu = 5, theta = 4) #Now fit and check that we get the right parameters fd <- fitdistr(x4, "Negative

I woud like to plot cumulative histograms. Specifically, I have data like Sex M M F M F F M F Height 6 6.3 6.1 5.5 7.2 6.2 5.9 6.0 .... and I want to plot a histogram of the distribution of all heights, colouring the histogram bars according to sex, for example | o | oo o | o oo ** o o = observations of women | o o*o***o * = observations of

I want to plot biplots. I have seen the function biplot, but there are some extra features I would like, that I do not know how to achieve. 1. My observations, and my variables, fall into groups. Is there a way to, say, plot the observations in several different colours, according to which group the observation falls into? Similarly with the variables? I am used to the lattice idiom, in which one

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

----- Original Message ----- From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz> To: <paul at datavore.com> Sent: Thursday, September 04, 2003 2:56 AM Subject: Re: [R] Overlaying graphs > I do not know how to overlay the curve graphic on top of hist graphic. > > Do you know about the "add=TRUE" option for plot()? > > I am hoping to show visually

I am having trouble with ESS+R. I don't know if it is an ESS problem or an R problem, so I'm posting to this mailing list in the first instance. I am using R 1.6.2, Windows XP (latest updates installed), XEmacs 21.4, and ESS 5.1.21. I have experienced the same problem under Windows 2000 and recent versions of Emacs and ESS (though my current Windows XP installation is from scratch, and

Full_Name: Robert Kushler Version: 2.7.2 OS: Windows XP Submission from: (NULL) (69.246.102.98) I believe that the negative binomial family (from MASS) should be added to the list for which dispersion is set to 1.

When I alter the levels of a factor, why does it alter the names too? f <- factor(c(A="one",B="two",C="one",D="one",E="three"), levels=c("one","two","three")) names(f) -- gives [1] "A" "B" "C" "D" "E" levels(f) <-

I get strange results when I try to format() an NA character string. > x <- 'sometext' > x[1] <- NA > format(NA, width=32) # displays sensibly, right-justified > format(x,width=32) # displays sensibly, left-justified > format(x,width=33) # displays "" > format(x,width=36) # R exits abnormally with code 5 > version platform i386-pc-mingw32

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

Dear List, I am trying to do model averaging for a negative binomial model using the package AICcmodavg. I need to use glm() since the package does not accept glm.nb() models. I can get glm() to work if I first run glm.nb and take theta from that model, but is there a simpler way to estimate theta for the glm model? The two models are: mod.nb<-glm.nb(mantas~site,data=mydata)