search for: gnlm

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 __________________________________ An...

...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 19 20 85 18 20 110 18 18 110 18 18 The bnlr{gnlm} documentation talks about a two column matrix as input. Thanks for any help Jens -- Jens Schmidt TU Dresden -Inst. f. Hydrobiologie- Tel. +49-(0)351-463-6915 Mommsenstrasse 13 Fax +49-(0)351-463-7108 01062 Dresden -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-...

...of these parameter values will produce an optimal fit. When it does so, it crashes because the equation can not have a negative value because its in a sqrt function. QUESTION: How do I restrict the optimisation algorithm's parameter space so it does not search negative values when using GNLM? Are there other Libraries that Fit Non-linear models and allow for one to control the parameter space the search algorithm is restricted by? Any help would be appreciated. Thanks Dom [[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...

I am attempting to fit discrete data (daily counts of arrivals of recreational vessels at locks on a river) using the fit.dist package. Some distributions return values of NaN and Inf for certain situations, an example with Inf values is shown below. # of vessels: 1 2 3 4 5 6 7 8 9 10 11 # of days with # of vessels: 35 20 10 5 6

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:

...bols from /lib/libc.so.5...done. Reading symbols from /lib/ld-linux.so.1...done. Reading symbols from /usr/local/src/R-0.64.1/library/rmutil/libs/rmutil.so...done. Reading symbols from /usr/local/src/R-0.64.1/library/mmdc33/libs/mmdc33.so...done. Reading symbols from /usr/local/src/R-0.64.1/library/gnlm/libs/gnlm.so...done. #0 compactPhase () at memory.c:599 599 switch (TYPEOF(s)) { /* get size in bytes */ (gdb) quit Patrick. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.htm...

...parameter values will produce an optimal fit. When it does so, it crashes because the equation can not have a negative value because its in a sqrt function. > > QUESTION: How do I restrict the optimisation algorithm's parameter space so it does not search negative values when using GNLM? Are there other Libraries that Fit Non-linear models and allow for one to control the parameter space the search algorithm is restricted by? There are two issues here: 1) Rendering optimization codes resistant to inadmissible parameter vectors (sqrt of negatives etc.) 2) Providing constraints,...

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 on to the packages I use regularly? Thanks in advance for any suggestions or insight, and a big thanks to all for creating and maintaining such a po...

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 parameters to the one used in fmr... I have these variables in the model: count: the number of logs in a foraging tray (this is the response variable). ta: the ambient...

R version: RW0632 1. gnlm package, nlr() error message: "Last global step failed to locate a point lower than x. Either x is an approximate local minimum of the function, the function is too non-linear for this algorithm, or steptl is too large." last line: steptol (also, should it read "steptol is too sm...

Can someone show me how to specify zero inflated poisson and zero inflated negative poisson models in R? I would like to replicate the example given in Long (1997: Regression Models for Categorical and Limited Dependent Variables) in Chapter 8.5 (pp. 242-247). TIA Dirk ************************************************* Dr. Dirk Enzmann Criminological Research Institute of Lower Saxony

I want to generate 1,000 random samples of sample size=1,000 from ZINB. I know there is a rnegbin() to generate random samples from NB, and I know I can use the following process: do i=1 to 1000 n=0 do i=1 to 1000 if runi(1)>0.1 then x(i) = 0; else x(i)=rnegbin(); n=n+1; if n>1000 then stop; end; output; end; Anybody can help me out with the R code? Thanks very much ahead of time.

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]]

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 accompanying the book "Statistical tools for nonlinear regression" by Hu...

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:

I have experimented with the cheese data example from McCullagh&Nelder, page 175. With a proportional odds model they obtain a residual deviance of 20.31. Estimating the same model with polr(MASS) gives a residual deviance of 762.11 !, while using ordglm(gnlm) gives a deviance of 523.94. Can anybody explain these differences? The data frame with the data are: > cheese Cheese N Response nResponse 1 A 1 III 2 2 A 7 IV 3 3 A 8 V 4 4 A 8 VI 5 5 A 19 VII...

...g 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), fit.dist(gnlm)) includes the quasipoisson distribution as one of their standard named distributions. Is the way forward to supply an appropriate density function to fitdistr, or is there some easier way to fit a quasipoisson distribution? Many thanks Nigel Harding Craigton Ecological Services 48 Craigend Drive...

...like to do. Any suggestions for some alternative methods such as those which are specially programmed for R and are suited to the type of data which I am analysing? I understand I will need a flexible repeated measures application to be able to accept the unequal sampling. I also will need a gnlm regression to fit the general data structure. And also, I think, the mixed models capability in order to fit in the true nature of the data (see next paragraph). Lastly, I have a question about independent variables. For example, I have a measure for host plant density at a site, which I am us...

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 of perhaps subtracting 1 from all the counts and then fitting the ZIP...