similar to: How does nlm work?

Hello, I would like to know if there is any package that allow us to fit Generalized Linear Models via Maximum Likelihood and Linear Models using Generalized Least Squarse in R as the functions glim and gls, respectively, from S-Plus. Also, anybody know if there is any package that fit Log-Linear Models using Generalized Least Squares? Any help will be very useful. Thanks, -- Frederico

Hello, I'm just start learning R/S-Plus, so I think this 2 doubts is going to be too easy for you... 1) I couldn't discover what is the command for a concatenation of 2 variable strings. 2) For example, if I have three variable strings, and each one has the name of a variable in a data matrix: a<-V1 b<-V2 c<-V3 , is it possible to construct a command like this:

Hi, I'm trying to fit a marginal model via GEE but I'm getting strange results and few problems. If I set the working correlation as exchangeable I'm getting the same fitting when I set as independent. Comparing to SAS results it shouldn't happen. If I try to use another working correlation (like AR-M or stat_M_dep), R just exits without giving any error message. Another doubt

I have a .csv table named mailing.csv as below. It consist a receiver, subject and sender. Receiver subject sender 1 Adrian Cole RE: [WHIRR-117] Composable services Tom White 2 Adrian Cole RE: [WHIRR-117] Composable services Tom White 3 Adrian Cole RE: [WHIRR-117] Composable services Adrian Cole 4 Adrian Cole RE: [WHIRR-117]

Hi, I have a table with data, as below: dput(table): structure(list(Adrian.Cole = c(0L, 0L, 0L, 0L, 0L, 0L), Alison.Wong = c(0L, 0L, 0L, 0L, 0L, 0L), Andrei.Savu = c(0L, 0L, 0L, 0L, 0L, 0L), Bruno.Dumon = c(0L, 0L, 0L, 0L, 0L, 0L), Edward.J..Yoon = c(0L, 0L, 0L, 0L, 0L, 0L), Eugene.Koontz = c(0L, 0L, 0L, 0L, 0L, 0L), Jakob.Homan = c(0L, 0L, 0L, 0L, 0L, 0L), Kelvin.Kakugawa = c(0L, 0L,

Dear useRs is there any nonparametric test for the analysis of variance in a design with two within-factors (repeated measures on both factors)? Friedman is not appropriate here, therefore I am grateful for any alternative test. thanks for any hint cheers christoph --

Hi, I have a script below. dat <- read.table(file="pt.csv", header=T, sep=",", row.names=1, col.names=1) dat for(which_col in seq_len(ncol(dat))) { subset_data <- dat[,which_col:ncol(dat)] file_name <- sprintf('%s.csv', colnames(dat)[which_col]) write.csv(subset_data, file_name) message(sprintf('Saving %s', file_name)) }

Bugs item #8039, was opened at 2007-01-21 01:51 You can respond by visiting: http://rubyforge.org/tracker/?func=detail&atid=411&aid=8039&group_id=85 Category: win32-pipe Group: Code Status: Open Resolution: None Priority: 3 Submitted By: David Koontz (dkoontz) Assigned to: Nobody (None) Summary: Cannot send data that includes a binary 0 Initial Comment: In pipe.c on lines 262 and

RubyStuf.com, the premiere place for Ruby stuff, has some new, um, stuff. http://www.rubystuff.com/ Profoundly inspired by a series of ruby-talk threads on just how "dangerous" is Ruby comes "Ruby: You''ll shoot your eye out" shirts and mugs: http://www.cafepress.com/ruby_dangerous http://www.cafepress.com/rubyshootout (A big shout-out to David Koontz for the

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

Dear useRs, I am analysing the behaviour of MLE for the two parameters of a kind of exponential distribution, leaving as initial values the estimators moments produced by the variation coefficient. I do using simulations, giving them an accountant, r. But running my codes remains a problem with the nlm function. To review details wearing On one of the lines put status

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

Dear R-experts! I've been using nls() for a while for fitting my data, approx 10,000 points long. Now, I have to use low-level minimizing functions to get more control on fitting process, so I tried nlm(). And, the fitting process became *much* more slower! The question is: why? why nls() works faster? Thank you very much! Timur.

Dear all, I just know how to solve an eaquation by using optim() or nlm(). But, now, I have three nonlinear equations, how could we use optim() or nlm() to solve  a system of nonlinear equations in R?  Thank you so much. Sincerely, Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

Hi, I had a hard time in learning nlm in R and appreciate any help. I encounted the following error message from time to time when I tried different starting parameter values (three parameter values in this case) in nlm(f=SS.fun,p=c(0.1/40,0.1,2),hessian = FALSE,N.measure=object,h=20) Error in f(x, ...) : only 0's may mix with negative subscripts Basically I know the three parameter

Hi I use both R and Splus to fit miximum likelihood models. In general these are Gaussian mixture models of various kinds. The ms (in Splus) and nlm (in R) generally provide us with the same results However nlm is not nearly as stable and reliable with respect to finding the min of a surface. Nlm on the other hand is faster. We have played with the various tol controls but have been unable to

Dear all, I am using the nlm(base) function for a simple least squares problem. The size (order of magnitude) of the minimized function near the final parameter set is normally about 1e-10. Using the fscale=1e-10 argument in nlm is inefficient (no minimization is performed, iterations=0 or 1). Nlm only works when the minimized function is multiplied by 1e10 (without changing the default

Out of curiosity, I have tried, without success, to use the new facility in nlm to specify the gradient and hessian. (It is many years since I had a problem simple enough to make analytic derivation of these worthwhile.) The help now says that the function must have attributes with these names but gives no indication as to what should be in the attributes. The online example and demo do not use

Dear R users, I am using the nlm function for minimization of the very non-linear function of four parameters. I am running 100 simulations and almost always I get the convergence code =2 (Successive iterates within tolerance. Current iterate is probably solution.) [about 75 times of 100]. Frequently, 3 of 4 relative gradients are close to zero and the fourth is huge but there are also cases