search for: reparametrisation

Hi, I posted a question (bellow) a few weeks ago and had a reply (thanks Christian) that partly solves the problem, but I still would like to be able to restrict some of the independent variables in a nls model to be always >0, (is there a way to do it)?? Thanks, Angel >From: "Christian Ritz" <ritz at dina.kvl.dk> >To: "Angel -" <angel_lul at

Hi, I am trying to fit a sigmoid curve to some data with nls but I am getting into some trouble. Seems that the optimization method is getting down to some parameter estimates that make the equation unsolvable. This is an example: >growth<-data.frame(Time=c(5,7,9,11,13,15,17,19,21,23,25,27),BodyMass=c(45,85,125,210,300,485,570,700,830,940,1030,1120))

Hello. I had previously posted a question concerning the optimization of a nonlinear function conditional on equality constraints. I was pointed towards the contrOptim function. However, I do not understand the syntax of this function with respect to specifying the constraints and so I don’t know if it is what I need. The command is: constrOptim(theta, f, grad,ui,ci,…). “theta” is the

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.