similar to: suggested modification to the 'mle' documentation?

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

I stumbled on the following: > library(stats4) > example(mle) > confint.default(fit2) Error in UseMethod("vcov") : no applicable method for "vcov" In addition: Warning message: In object$coefficients : $ operator not defined for this S4 class, returning NULL > vcov(fit2) lymax lxhalf lymax 0.02857612 -0.04870231 lxhalf -0.04870231 0.11457338

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

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)

Hi! This is a repost of an earlier message (with a clearer example demonstrating the problem I ran into). If you run the mle example in stats4 library(stats4) 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)) (fit <- mle(ll)) plot(profile(fit),

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,

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

Full_Name: Elizabeth Purdom Version: 1.9.1 OS: Windows XP Submission from: (NULL) (171.64.102.199) It would be nice if names(obj) would give slot names as well. Since for many people slots are new, the first thing that happens is you try to access what's in them and can't find how to do it. If you don't know that slotNames() exists, it can be very frustrating. Moreover, if you

Hello, I am new to R and I am trying to estimate a discrete model with three choices. I am stuck at a point and cannot find a solution. I have probability functions for occurrence of these choices, and then I build the likelihood functions associated to these choices and finally I build the general log-likelihood function. There are four parameters in the model, three of them are associated to

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

Hello, i have coded the following loglikelihood-function # Log-Likelihood-Funktion loglik_jm<-function(N,phi,t) { n<-length(t) i<-seq(along=t) s1<-sum(log(N-(i-1))) s2<-phi*sum((N-(i-1))*t[i]) n*log(phi)+s1-s2 } # the data t<-c(7,11,8,10,15,22,20,25,28,35) # now i want to do a 3d-plot and a contourplot in order to see at which values of N and phi the loglikelihood

Hello to all, I'm a biologist trying to tackle a "fish" (Poisson Regression) which is just too big for my modest understanding of stats!!! Here goes... I want to find good literature or proper mathematical procedure to calculate a confidence interval for an inverse prediction of a Poisson regression using R. I'm currently trying to analyse a "dose-response"

I need to repeat a function many times, with differing parameters held constant across iterations. To accomplish this, I would like to create a list (or vector) of parameters, and then insert that list into the function. For example: q<-("l,a,b,s") genericfunction<-function(q){ } ###### The equivalent code would of course be genericfunction<-function(l,a,b,s){ } Any help

Hi there, I'm pretty new to the field of fitting (anything). I try to fit a distribution with mle, because my real data seems to follow a zero-inflated poisson distribution. So far, I tried a simple example to see whether I understand how to do it or not: # example count data x <- 0:10 y <- dpois(x, lambda = 1.4) # zero-inflated poisson zip <- function(x, lambda, prop) { (1 -

Hi all, Sorry for the re-post, I sent my previous e-mail before it was complete. I am trying to model seroprevalence using the differential equation: dP/dt = beta*seronegative*.001*(seropositive)-0.35*(0.999)*(seropositive)-r*seropositive. I would like to estimate my two parameters, beta and r, using maximum likelihood methods. I have included my code below:

A few day ago, I was looking for an answer to my question but didn't get one. Anybody who can help now? Hello, I tried to use mle to fit a distribution(zero-inflated negbin for count data). My call is very simple: mle(ll) ll() takes the three parameters, I'd like to be estimated (size, mu and prob). But within the ll() function I have to judge if the current parameter-set gives a nice

Hello, I tried to use mle to fit a distribution(zero-inflated negbin for count data). My call is very simple: mle(ll) ll() takes the three parameters, I'd like to be estimated (size, mu and prob). But within the ll() function I have to judge if the current parameter-set gives a nice fit or not. So I have to apply them to observation data. But how does the method know about my observed

R 2.3.0 Linux, SuSE 10.0 Hi I have two problems with mle - probably I am using it the wrong way so please let me know. I want to fit different distributions to an observed count of seeds and in the next step use AIC or BIC to identify the best distribution. But when I run the script below (which is part of my original script), I get one error message for the first call of mle: Error in

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 want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same