similar to: Fit a nonlinear regression model with power exponentially distributed errors

> 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

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

Hi, I found this email on the R website. I am trying to figure out how to analyse a data set that I believe will need to be run through a procedure involving repeated measures, regression and mixed models. The data is of insect populations (dependent variable - either 0/1=binomial, or as counts=poisson) in sites with different characteristics (multiple independent variables which are both

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

Hi list, I'd like to fit generalized gamma and weibull distributions to a number of data sets. I've been searching around and found references to R and Jim Lindsey's GNLM package, which has the gnlr and gnlr3 procedures that can do this. Now, I'm completely new to R, and I'm working my way through the introduction... Nevertheless, I'd like to ask if someone could post

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

Hi all, I'm trying to calculate a nonlinear regression to get a simpler expression for a complicated formula. I expect that a function of the type 1 ---------------------------- - (a + b1*x + b2*y) 1 + e should be a good fit. Now I'm trying to calculate a regression with R and must admit that I'm slightly confused by the variety of possibilities to

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

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

Dear all, I am analyzing counts of seabirds made from line transects at sea. I have been fitting Poisson and negative binomial distributions to the data using the goodfit function from the vcd library. I would also like to evaluate how well a quasi-poisson distribution fits the data. However, none of the potentially suitable functions I have identified (goodfit(vcd), fitdistr(MASS),

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:

Hi, I'm not yet familar with GLM and still learning. How can I perform a BNL (to estimate LDp values) with matrixes like this (N indicates the observed objects): data from: Kerr D, Meador J. 1996. "Modeling Dose Response Using Generalized Linear Models". Environ Toxicol Chem 15(3) conc respond n 0 0 20 0 1 19 0 1 20 0 0 20 13.5 5 23 13.5 2 20 29 9 20 29 6 20 53 12 20 53 15 20 85

I just had a memory crash with R-0.64.1. I am running under intel pentium 200mhz under slackware linux 2.0.30. 1/home/plindsey >gdb /usr/local/src/R/bin/R.binary core GDB is free software and you are welcome to distribute copies of it under certain conditions; type "show copying" to see the conditions. There is absolutely no warranty for GDB; type "show warranty" for

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

Dear all, Are there functions or packages in R that can handle interval-censored data? I have looked in various packages (such as survival5 or event), but it seems that only right-censored data can be analysed. More generally, are there methods to analyse both interval-censored observations and right-censored observations in the same data set? Thanks in advance. Emmanuel Paradis

Basic algebra and exponentials/logs. I leave those details to you or another HelpeR. -- Bert On Sun, Aug 20, 2023 at 12:17?PM Paul Bernal <paulbernal07 at gmail.com> wrote: > Dear Bert, > > Thank you for your extremely valuable feedback. Now, I just want to > understand why the signs for those starting values, given the following: > > #Fiting intermediate model to get

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

Dear Bert, Thank you for your extremely valuable feedback. Now, I just want to understand why the signs for those starting values, given the following: > #Fiting intermediate model to get starting values > intermediatemod <- lm(log(y - .37) ~ x, data=mod14data2_random) > summary(intermediatemod) Call: lm(formula = log(y - 0.37) ~ x, data = mod14data2_random) Residuals: Min

Hi everyone, How might I go about configuring R to keep add-on packages loaded from session to session? Is this undesireable for some reason? At present, I keep a file called "pckgs.txt" in my working directory with, e.g., library(gnlm) library(Hmisc) ... and then type source("add-ons.txt") every time I start a new session. I suspect there's a more elegant way to hold

Hello, I want to perform a nonlinear curve fit in order to obtain parameter estimates from experimentally determined data (y in dependence of x), but with an implicit function, thus, a function of which I cannot isolate y on the left-hand side of the equation. As far as I understand, the functions I found up to now (nls, optim) all work only for explicit functions. My data looks like