similar to: Help with sigmoidal quasi-poisson regression using glm and gnm functions

Hi all I want to construct a generalised nonlinear model (binomial family) using gnm, of the form: Response = a + b variable1 + c variable2 + d variable3 - d b variable4 - d c variable5, with the parameters b, c, and d appearing more than once. Hence, I think I need to use a custom nonlin function with gnm. One of my predictor variables is categorical, so I have created a dummy variable for

Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use. The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the

Hi- I'm a biologist trying to figure out the growth rate of salamanders in different ponds. I collected individuals from various populations at different dates, and using the size and date collected, I want to figure out the growth curve of each population. My question is: How do I fit my data to a Gompertz function in R? Thank you so much! Sarah -- View this message in context:

Hi, I am quite new to R and would like to do nonlinear regressions with Poisson distributed data. I would like to estimate paramters of an equation of this type: FR = [c*NO * exp(a+b*NO)] / [(c+NO)*(1+exp(a+b*NO))] a,b and c are parameters, NO are input values I found both the gnm and gnlr3 function which should be able to do this regression but I can't manage to make it work. How can I

Hi everyone! In a package I'm developing, I have created a custom function to get jackknife standard errors for the parameters of a gnm model (which is essentially the same as a glm model for this issue). I'd like to add support for bootstrap using package boot, but I couldn't find how to proceed. The problem is, my data is a table object. Thus, I don't have one individual per

Hi, I'm trying to make multiple poisson regressions with the MLE2 command. I have used the following expression, but I receive an error message: poisfit <- mle2(y ~ dpois(exp(b0 + b1*x1 + b2*x2)), start=list(b0=1, b1=1, b2=1), data=data1) Error in optim(par = c(1, 1, 1), fn = function (p) : non-finite initial value 'vmmin' I have changed initial values using coefficient values

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

I am attempting to run a Poisson EWMA model using Patrick Brandt's source code. I get the following error when I run the code: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent Dimnames(x) looks like this: [[1]] NULL [[2]] [1] "mip" "div" "nom" "unity" "mood"

I am trying to write a function that fits a sigmoid given a X and Y vector guessing the start parameters. I use nls. What I did (enclosed) seems to work well with many data points but if I want to fit small vectors like : pressure <- c(5,15,9,35,45) gas <- c(1000,2000,3000,4000,5000) it do not work. The help page says that it do no not work on zero residual data. Massimo Cressoni

Hello all, I would like to get parameter estimates for different models. For one of them I give the code in example. I am estimating the parameters (i,j and k) with the nls function, which sees the error distribution as normal, I would however like to do the same as nls with the assumption that the errors are poisson distributed. Is there a way to do this with R? Are there packages designed

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

Sorry about re-posting this, it never went out to the mailing list when I posted this to r-help forum on Nabble and was pending for a few days, now that I am subscribe to the mailing list I hope that this goes out: I've been a viewer of this forum for a while and it has helped out a lot, but this is my first time posting something. I am running glm models for richness and abundances. For

Hello, I'm looking for a dataset for Quasipoisson regression. The result must be significantly different from the classic poisson regression. You can help me? Please It is for my last university exam Thanks a lot -- View this message in context: http://r.789695.n4.nabble.com/Dataset-Quasi-Poisson-tp3533060p3533060.html Sent from the R help mailing list archive at Nabble.com.

Hi list, can anybody point me to the trick how glm is computing the dispersion parameter in quasi-poisson regression, eg. glm(...,family="quasipoisson")? Thanks &regards, Sven

Dear list, I am using glm's to predict count data for a fish species inside and outside a marine reserve for three different methods of monitoring. I run glms and figured out the best model using step function for each methods used. I predicted two values for my fish counts inside and outside the reserve using means of each of the covariates (using predict() ) therefore I have only one value

Hi everyone I am stuck on specifying my own maximum likelihood function for a special poisson model. My poisson model is as follow: O ~ Pois(b*N + b*RR*E) With O = observed cases b = constant (known) N = number of unexposed persons (known) E = number exposed persons (known) RR = relative risk (value is assumed under a scenario, e.g. RR=2.0) I used rpois to simulate the values of O for several

The NEWS for R 2.3.0 states that "R CMD check works for packages whose package name is different from the directory name in which it is located." However that hasn't been my experience. I ran R CMD check on package sources located in a directory with the same name as the package and it worked as expected. Then I renamed the directory and tried again. The first attempt got stuck

Dear R Users, I'm working on a problem where I have a multivariate response vector of counts and a continuous predictor. I've thought about doing this the same way you would do a Multvariate regression model with normally distributed data, but since these data are counts, they are probably better modeled with a Poisson distribution. For example y1<-rpois(100,3.5) y2<-rpois(100,1.5)

Hi, I'm totally new to R so I apologise for the basic request. I am looking at the incidence of a disease over two time periods 1990-1995 and 2003-2008. I have counts for each year, subdivided into three disease categories and by males/females. I understand that I need to analyse the data using poisson regression and have managed to use the pois.daly function to get age-sex adjusted rates and