similar to: gnlr/3 question

Hi list, my previous question was obviously too basic to deserve an answer - apologies for that. I'm learning, things can only get better :-) My current problem is with fitting a generalized gamma distribution with an additional "shift" parameter, that represents a shift of the distribution along the X axis. The gnlr3 function (in Jim Lindsey's GNLM package) fits this

John Fox wrote <<< >From your description, it seems possible that there are too many zeros for a Poisson or negative-binomial model. Since the focus of your paper is the methodology, you might want to try a zero-inflated Poisson or negative-binomial model. Though I haven't tried them, I'm aware of two sources of R functions for zero-inflated count models -- zeroinfl(), from

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

Is there a simple say of extracting the shape parameter from gnlr? The return given is the actual function rather than the value. I have looked at all the values returned by names(gnlr). ie gmod<-gnlr(data,....) gmod$shape returns the function definition rather than the value found. Simple case I am using it is for censored normal data (rather simpler than survival4 it seems). The value

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

How to fit a nonlinear regression model with power exponentially distributed errors? I know gnlm has a function gnlr3 that could work, but I would be grateful if example R code is provided. Daniel [[alternative HTML version deleted]]

Pursing my earlier question, when I tried loading Lindsey's gnlm, I got a message Loading required package: rmutil Warning message: There is no package called 'rmutil' in: library(package, character.only = TRUE, logical = TRUE, warn.conflicts = warn.conflicts, According to the R documentation http://finzi.psych.upenn.edu/R/doc/html/packages.html rmutil is in the standard

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:

> On Wed, 2 Feb 2000, Adriane Leal wrote: > > > I'd like to perform a box-cox transformation to a data set and also plot > > lambda versus L(lambda) using R. Does anybody knows how can I do such a > > thing? gnlr3 in my gnlm library does both linear and nonlinear models with Box-Cox transformation. However, it is somewhat nonstandard as it renormalizes to obtain a

Dear List Members, I need a bit help about fitting some theoretical distributions (such as geometric, exponential, lognormal or weibull distribution) to the following *dry spell*, *wet spell*, *cycles (Wet-Dry or Dry-Wet)* from my meteorological (daily rainfall) data http://www.angelfire.com/ab5/get5/R.rainfall.txt only for rainy seasen (july - september) of 14 years only:

R Users, Jim Lindsey's R packages: * event: Event History Procedures and Models * gnlm: Generalized Nonlinear Regression Models * growth: Multivariate Normal and Elliptically-Contoured Repeated Measurements Models * repeated: Non-Normal Repeated Measurements Models * rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models * stable: Probability Functions and

R Users, Jim Lindsey's R packages: * event: Event History Procedures and Models * gnlm: Generalized Nonlinear Regression Models * growth: Multivariate Normal and Elliptically-Contoured Repeated Measurements Models * repeated: Non-Normal Repeated Measurements Models * rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models * stable: Probability Functions and

Hello I am interested in Poisson or (ideally) Negative Binomial regression with an inflated number of 1 responses I have seen JK Lindsey's fmr function in the gnlm library, which fits zero inflated Poisson (ZIP) or zero inflated negative binomial regression, but the help file states that for ' Poisson or related distributions the mixture involves the zero category'. I had thought

Hi, I just installed the odesolve package and ran the check command on it. It failed trying to execute library(gnlm). Sure enough, there is no gnlm library on my system and I could not find it on the CRAN archive either. Am I missing anything obvious or is gnlm some private library that somehow found its way into the example section of odesolve? Thanks in advance, Andy

Beste tinc-developer, Ik moet van mijn baas een aantal VPN's aanleggen en ben nu thuis bezig om te testen. Ik heb 2 computers in een thuisnetwerkje (totaal 6 computers) geinstalleerd met SuSe 7.3, daar de klanten dit ook hebben. De computers zijn verbonden met een switch. Dit is de inhoud van mijn bestanden: Op computer 1 genaamd suse met ip 192.168.0.99 netmask 255.255.255.0

I have a complicated nonlinear function, myfun(a,b,c), that I want to fit to data, allowing one or more of the parameters a, b, and c in turn to have linear dependence on other covariates. In other words, I'd like to specify something like nls(y~myfun(a,b,c),linear=list(a~f1,b~1,c~1)) I know would this work in nlme *if I wanted to specify random effects as well*, but I don't -- and

Dear friends. Do Polr in Mass change the sign of the coefficients ? Example (McCullagh 1980) options(contrasts=c("contr.treatment","contr.poly")) library(Mass) freq <- c(19,29,24,497,560,269) yy <- ordered(gl(3,1,6)) z4 <- polr(yy~x,weights=freq) > z4 Call: polr(formula = yy ~ x, weights = freq) Coefficients: x2 -0.6026492 Intercepts: 1|2

Dear R-group members, I use: platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 4.1 year 2002 month 01 day 30 language R I try to fit a 2 compartment model. The compartments are open, connected to each other and

I have some data which I am trying to fit with a negative binomial distribution. I have found the glm.nb function from MASS. I have reason to believe that the mean parameter mu depends on certain factors, and that the shape parameter theta depends on others. If, say, the factors are P and Q, it might be that mu ~ P:Q and theta ~ P (where mu ~ P:Q means that mu is a function of the pair (P,Q))

Hello R users, Doing My PhD I collected count data which I believe is zero inflated. I have run a statistical model with lmer and family=poisson and got summary(model)@sigma=1 so I believe there is no overdispertion. I would like to use the fmr function from the 'gnlm' library but I just cannot figure out from the examples in the help page and some forums out there how to convert the lmer