search for: reparametrised

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

...and e is the error term. I realised that R doesn't have a built-in function to estimate truncated regression models as does STATA, LIMDEP etc. I tried the survival and FEAR packages and couldn't fit it for my case. So I wrote the log likelihood function of the truncated regression model and reparametrised it using Olsen (1978) so that the function is globally concave and has an unique maximiser. I used a quasi-Newton method (BFGS) to maximise my function. I also used Newton-Raphson method (maxNR) to maximise my function. The (naive) code can be seen below. sw1<-function(theta,dhat,z) { gamma<...

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.