similar to: nls profile with port/constraints

[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

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) {

PR #7690 points out that if the confidence intervals (+/-1.58 IQR/sqrt(n)) in a boxplot with notch=TRUE are larger than the hinges -- which is most likely to happen for small n and asymmetric distributions -- the resulting plot is ugly, e.g.: set.seed(1001) npts <- 5 X <- rnorm(2*npts,rep(3:4,each=npts),sd=1) f <- factor(rep(1:2,each=npts)) boxplot(X~f) boxplot(X~f,notch=TRUE) I can

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

I found what seems to be a glaring bug in nls when using constraints, but it is so glaring that I'm a bit nervous about having been stupid. I have (1) tried to make sure I'm up to date: platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 2.0 year 2005 month 10 day 06 svn rev 35749 (2) skimmed through the SVN

Hi, I'm getting an error that I don't understand when updating an nls object. Here is a toy example. dd <- structure(list(Contrast = c(0.00376, 0.03759, 0.12782, 0.25564, 0.50376, 1), Response = c(0.29915, 6.13248, 29.01709, 30.0641, 29.46581, 27.67094)), .Names = c("Contrast", "Response"), class = "data.frame", row.names = c(NA, -6L)) m1 <-

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

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 [Sorry to those who don't like it for top-posting] Thierry, I'm curious whether this addresses your problem (although we don't have a hard timetable for the next release [it has to avoid conflicts with the 3.2.0 release in 2.5 weeks at the very least], so this might be problematic if your package needs to depend on it). I'm

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

>>>>> Steve Martin >>>>> on Mon, 18 Dec 2023 07:56:46 -0500 writes: > Does mFUN() really need to be a function of x and the NA values of x? I > can't think of a case where it would be used on anything but the non-NA > values of x. > I think it would be easier to specify a different mFUN() (and document this > new argument)

I need to find the area under a trapezoid for a research-related project. I was able to find the area under the trapezoid in MATLAB using the code: function [int] = myquadrature(f,a,b) % user-defined quadrature function % integrate data f from x=a to x=b assuming f is equally spaced over the interval % use type % determine number of data points npts = prod(size(f)); nint = npts -1; %number of

I'm very excited by the new mle package now incorporated in stats4. If possible, I'd like to help develop it. In the past I wrote a similar package (mleprof, available from http://www.zoo.ufl.edu/bolker/R/src), and would like to see if there's anything that my package does that I could contribute (in particular, I'd like to make sure that the code is as robust as possible in

As I've mentioned here, before, I'm working on an extended version of mle(), a function from the stats4 package that's a wrapper for optim(). I'd like (against the advice of Peter Dalgaard -- sorry) to incorporate a "data" argument, similar to the arguments in lm, nls, nlme, etc., that would allow the log-likelihood function to be evaluated with different sets of data.

Sorry I had typo in previous email, this typo corrected version: Dear R experts I have simple question, please execuse me: #example data, the real data consists of 20000 pairs of variables K1 <- c(1,2,1, 1, 1,1); K2 <- c(1, 1,2,2, 1,2); K3 <- c(3, 1, 3, 3, 1, 3) M1a <- rep( K1, 100); M1b <- rep(K2, 100) M2a <- rep(K1, 100); M2b <- rep(K1, 100) M3a <- rep(K1, 100); M3b

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 15-03-23 12:55 PM, Thierry Onkelinx wrote: > Dear Ben, > > Last week I was struggling with incorporating lme4 into a package. > I traced the problem and made a reproducible example ( > https://github.com/ThierryO/testlme4). It looks very simular to > the problem you describe. > > The 'tests' directory contains

Hi there I'm trying to find the mle fo a multinomial model ->*L(N,h,S?x)*. There is only *N* I want to estimate, which is used in the number of successes for the last cell probability. These successes are given by: p^(N-x1-x2-...xi) All the other parameters (i.e. h and S) I know from somewhere else. Here is what I've tried to do so far for a imaginary data set:

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Does mFUN() really need to be a function of x and the NA values of x? I can't think of a case where it would be used on anything but the non-NA values of x. I think it would be easier to specify a different mFUN() (and document this new argument) if the function has one argument and is applied to the non-NA values of x. zapsmall <- function(x, digits = getOption("digits"),

I'm in a bit beyond my depth with environments and such. The environment of a particular function, which I've set so it should have the things it needs, seems to be getting "lost" at some point during a call sequence. It's hard to come up with a _simple_ reproducible example, although if anyone's sufficiently interested I can post the package somewhere -- with the

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