similar to: strategies for incorporating a data= argument

I'm trying to figure out the best way of fitting the same negative log-likelihood function to more than one set of data, using mle() from the stats4 package. Here's what I would have thought would work: -------------- library(stats4) ## simulate values r = rnorm(1000,mean=2) ## very basic neg. log likelihood function mll <- function(mu,logsigma) {

Sorry to report further difficulties with nls and profiling and constraints ... the problem this time (which I didn't check for in my last round of testing) is that the nls profiler doesn't seem to respect constraints that have been set when using the port algorithm. See test code below ... If I can I will try to hack the code, but I will probably start by redefining my function with

A while back, I wrote to the list/engaged in some debate with Peter Dalgaard about the mle() function in the stats4 package -- in particular, I wanted it to have a data= argument so that parameters could be estimated for different sets of data with the same minuslogl function: Peter disagreed, suggesting that a function-defining-function (e.g. something like minusloglfun <- function(data) {

[posted to R-devel, no discussion: resubmitting it as a bug, just so it gets logged appropriately] Sorry to report further difficulties with nls and profiling and constraints ... the problem this time (which I didn't check for in my last round of testing) is that the nls profiler doesn't seem to respect constraints that have been set when using the port algorithm. See test code

Hi the list, I would need some advice on something that looks like a FAQ: the possibility of providing vectors to optim() function. Here is a stupid and short example summarizing the problem: -------------------------------- example 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <-

Full_Name: S?bastien Villemot Version: 2.4.0 OS: Debian testing Submission from: (NULL) (62.212.121.128) Hi, In the mle() function of the stats4 package, there is a bug in the ordering of the variables given in the 'start' argument. By just changing the order of the variables listed in the 'start' list (the initialization values), it is possible to obtain different estimation

I sent this in first on 30 July. Now that UseR! is over I'm trying again (slightly extended version from last time). With R 2.5.1 or R 2.6.0 (2007-08-04 r42421) "L-BFGS-B" behaves differently from all of the other optim() methods, which return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. This is not a bug in

I get the following error: Error in backSpline.npolySpline(sp) : spline must be monotone Has anyone had this error before? any ideas on a workaround? > > vols=read.csv(file="C:/Documents and Settings/Hugh/My > Documents/PhD/Swaption vols.csv" + , header=TRUE, sep=",") > X<-ts(vols[,2]) > #X > > > dcOU<-function(x,t,x0,theta,log=FALSE){ +

So, I've embarked on my threatened modifications to the mle subset of the stats4 package. Most of what I've done so far has *not* been adding the slick formula interface, but rather making it work properly and reasonably robustly with real mle problems -- especially ones involving reasonably complex fixed and default parameter sets. Some of what I've done breaks backward

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Hi there, When I do bootstrap on a maximum likelihood estimation, I try the following code, however, I get error: Error in minuslogl(alpha = 0, beta = 0) : object "x" not found It seems that mle() only get data from workspace, other than the boot.fun(). My question is how to pass the data to mle() in my case. I really appreciated to any suggestions. Best wishes, Jinsong

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

my problem actually arised with fitting the data to the weibulldistribution, where it is hard to see, if the proposed parameterestimates make sense. data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 how am I supposed to know what starting values i have to take? i get different

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string

Thank you very much. now, i call mle(minuslogl=loglik, start=start, method <<- method, fixed=list()) in the mle.wrap() function, and the profile.mle() worked. however, it created a variable named "method" in user workspace. if there had been a variable with same name, then the value of that variable would be destroyed. Is there a way to avoid that happen? Thanks again.

Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me. thank you for you atention. Bernardo. > erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE) > head(erizo) EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5 > TAN <-

Dear all, I just found that some of the packages are not able to load any more, after I installed R2.0.1 in my Mac, it even affects my old R1.8 installs. It gives me errors when I load packages that contains "myEnv" settings. such as: RMySQL, DBI, Rggobi, etc. But others that does not require "myENV" is all right, like tcltk that only calls the c functions. The errors

Dear all, I'd appreciate any help to rectify what must be a misconception of mine how environments work: ########################## myEnv <- new.env() myEnv$a.env <- 1 myEnv$symbols.env <- "a.env" a.global <- 2 symbols.global <- "a.global" myFun <- function(symbols){do.call("print", lapply(symbols, FUN=as.name))} do.call("myFun",

Dear Wolfgang, I think this new behaviour is related to the following R 4.0.0 NEWS item: > S3 method lookup now by default skips the elements of the search path between the global and base environments. Your environment "myenv" is attached at position 2 of the search() path and thus now skipped in S3 method lookup. I have just noticed that