similar to: gnm and gnlr3

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

Hello all, I am estimating parameters for regression functions on experimental data. Functional response of Rogers type II. I would like to know which points of my dataset are outliers. What is the best method to do this with R? I found a method via R help, but would like to know if there are better methods for my purpose. Here is the script I us now: library("mvoutlier") dat

Hi all, When I try to estimate the functional response of the Rogers type I equation (for the mle2 you need the package bbmle): > RogersIbinom <- function(N0,attackR2_B,u_B) {attackR2_B+u_B*N0} > RogersI_B <- mle2(FR~dbinom(size=N0,prob=RogersIbinom(N0,attackR2_B,u_B)/N0),start=list(attackR2_B=4.5,u_B=0.16),method="Nelder-Mead",data=data5) I get following error message

Hi all, I would like to predict some values for an nls regression function (functional response model Rogers type II). This is an asymptotic function of which I would like to predict the asymptotic value I estimated the paramters with nls, but can't seem to get predictions for values of m choice...... This is my script: RogersII_N <-

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

Hi all, I am doing nls regression of the Rogers type III equation. However I can't find good starting values and keep getting the error messages: "Missing value or an infinity produced when evaluating the model" OR "singular gradient matrix at initial parameter estimates" Is there a way to automatically check different starting values and give a list of values for each

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 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

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 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 everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 +

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]]

> 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! Some time ago, I replaced calls to library() with calls to requireNamespace() in my package logmult, in order to follow the new CRAN policies. But I just noticed it broke jackknife/bootstrap using several workers via package parallel. The reason is that I'm running model replicates on the workers, and the formula includes non-standard terms like Mult() which are provided by gnm. If gnm

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

Hi Milan, I expect I may be able to do something about the way the terms are evaluated, to ensure the evaluation is done in the gnm namespace (while still ensuring the variables can be found!). In the meantime, I think the following will work: Mult <- gnm::Mult f <- Freq ~ Eye + Hair + Mult(Eye, Hair) gnm::gnm(f, family=poisson, data=dat) Hope that helps, Heather On Wed, Apr 29, 2015,

In checking my vcdExtra package, the following NOTE newly appeared (R-Forge, using R version 3.0.1 Patched (2013-08-20 r63635)) Package in Depends field not imported from: ?gnm? These packages needs to imported from for the case when this namespace is loaded but not attached. In the DESCRIPTION file, I have Depends: R (>= 2.10), vcd, gnm (>= 1.0.3) In NAMESPACE: # we are a vcd

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 not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta