similar to: Problem with mle in stats4 (R 1.9.1)

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

With the exception of "L-BFGS-B", all of the other optim() methods return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. I don't think this is a "bug" in L-BFGS-B (more like a response to an undefined condition), but it leads to a bug in stats4::mle -- a spurious error saying that a better fit has been

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

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) { loglik.fun <- match.fun(FUN)

Hello: I wish to again express my appreciation to all who have contributed to making R what it is today. At this moment, I'm particularly grateful for whoever modified the 'mle' code so data no longer need be passed via global variables. I remember struggling with this a couple of years ago, and I only today discovered that it is no longer the case. I'd

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

The mle2 function (bbmle library) gives an example something like the following in its help page. How do I access the coefficients, standard errors, etc in the summary of "a"? > x <- 0:10 > y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) > LL <- function(ymax=15, xhalf=6) + -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) > a <- mle2(LL,

Dear all, I'd like tweaking the ... arguments that one user can pass in my function for fitting a model. More precisely, my objective function is (really) problematic to optimize using the "optim" function. Consequently, I'd like to add in the "control" argument of the latter function a "ndeps = rep(something, #par)" and/or "parscale =

I am using mle as a wrapper from optim( ). How would I extract the convergence code, to know that optim( ) converged properly? Thanks, Stephen Collins, MPP | Analyst Global Strategy | Aon Benfield [[alternative HTML version deleted]]

Dear All, There are two things about mle() that I wasn't so sure. 1) can mle() handle vector based parameter? say ll<-function(theta=rep(1,20)){..............} I tried such function, it worked for "optim" but not for "mle". 2) is there a general suggestion for the maximum number of parameters allowed to use in mle() or optim()? Thank you. Regards, MJO

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

Hi I am trying to recover the hessian of a problem optimised with box-constraints. The problem is that in some cases, my estimates are very close to the boundary, which will make optim(..., hessian=TRUE) or optimHessian() fail, as they do not follow the box-constraints, and hence estimate the function in the unfeasible parameter space. As a simple example (my problem is more complex though,

I'm looking for advice on manipulating parameters that are going to be passed through to another function. Specifically, I am working on my version of "mle", which is a wrapper for optim (among other optimizers). I would prefer not to replicate the entire argument list of optim(), so I'm using ... to pass extra arguments through. However: the starting values are

Can anyone help me with lbfgsb (function for optimization)? It takes the following parameters: void lbfgsb (int n, int lmm, double *x, double *lower, double *upper, int *nbd, double *Fmin, optimfn fn, optimgr gr, int *fail, void *ex, double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT); What do I put for parameter ex (11th parameter)? I looked at

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

Dear R users, I have a problem for which my objective function depends on both discrete and continuous arguments. The problem is that the number of combinations for the (multivariate) discrete arguments can become overwhelming (when it is univariate this is not an issue) hence search over the continuous arguments for each possible combination of the discrete arguments may not be feasible. Guided

Hello, I also try to fit a skewed distribution (like skewed student t) to data points. Do you have an idee howto do this??? thank you fabrice dusonchet *********************************************************************************** This email and any files transmitted with it are confidentia...{{dropped}}

?optim says, in describing the control parameter, 'fnscale' An overall scaling to be applied to the value of 'fn' and 'gr' during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on 'fn(par)/fnscale'. 'parscale' A vector of scaling values for the parameters.

Hi there, i''m building a small NAS with 5x1TB Disks. The disks contains at the moment some data, ntfs as the fs and aren''t a raid. Now my im wondering if its possible to add the parity later. So that i add step by step one disk to the pool. And when i add the last disk, i enable the parity. (i have only one another 1 tb disk to backup the files) Thank you for you replies and

Hi, I wantto use 'mle' function in R on linux. As I see its been integrated into the stats4 package. Am I correct ? If yes, Can anyone suggest how to install the stats4 package to be able to run 'mle' function in R on linux ? Otherwise how to sort out this problem ? Thanks Himanshu [[alternative HTML version deleted]]