similar to: Why different results with different initial values for MLE (optim)!

Hello, I'm maximizing a likelihood function with the function "optim", but for different intial parameters (in the input of the optim funtion) , I found different value for the likelihood function and the parameters estimates, the causes is that the algorithm has not found the global maximum for the function but only a local maximum. What must I do to obtain the global maximum for

I used the command below, but R gives me the error message--syntax error. can anyone see the mistakes I made? optimize(function(x,y) + ((327.727-(1-0.114^10)*y*(1-x)/x/(1-x^y))+(9517.336-327.727 *(1+(1-x)*(1+y)/x-327.727)))^2 + interval=c(0,1)) At the same time, I use nlm() but R gives me the code $code [1] 3 function(vals) { x <- vals[1] y <- vals[2]

Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is

Folks, I'm kind of newbie in R, but with some background in Matlab and VBA programming. Last month I was implementing a Maximum Likelihood Estimation in Matlab, but the algorithms didn't converge. So my academic advisor suggested using R. My problem is: estimate a mean reverting jump diffusion parameters. I've succeeded in deriving the likelihood function (which looks like a gaussian

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the

Dear: I am trying to work out the regularised incomplete beta function in R. I searched google on this and found UCS library. But I can not find this in R packages. Does anyone have use this before or how to insert UCS in R? Many Thanks! Xin Xin ------- Dr.Xin Shi Senior Lecturer in Statistics Manchester Metropolitan University Business School Aytoun Building Aytoun Street Manchester M1

Hi, I am currently trying to execute the following command: f<-factanal(factors=k$Components$nparallel,covmat=m,n.obs=2287,rotation="varimax",control=list(opt=list(method=c("BFGS")))) but keep getting the error: L-BFGS-B needs finite values of 'fn' I can't figure out what I am doing wrong here, why isn't optim being told to use BFGS instead of L-BFGS-B...

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]]

Hi, I have noticed that there is a change in the use of ellipses or . in R versions 2.6.1 and later. In versions 2.5.1 and earlier, the . were always at the end of the argument list, but in 2.6.1 they are placed after the main arguments and before method control arguments. This results in the user having to specify the exact (complete) names of the control arguments, i.e. partial matching is

On Sat, Jun 11, 2016 at 08:51:56PM +0300, Pavel Butsykin wrote: > The appliance memory size of 500 MB is insufficient in certain scenarios. In > partiuclar, semodule (which may be called by a guest tools uninstaller) often > triggers OOM (see https://bugzilla.redhat.com/426775) > > Increase it to 768 MB. > > Signed-off-by: Pavel Butsykin <pbutsykin@virtuozzo.com> >

I have used the following code to obtain a max likelihood estimator for a logit regression. The final command invokes ‘optim’ to obtain the parameter estimates. The code works OK but I want to use the ‘mle’ function in the ‘stats4’ package instead of directly calling ‘optim’. Can someone please figure out the command to do this? Thank you in advance. Martin # mlelo.r - maximum

Dear: I want to plot barplot and let bar be in the middle of each x axis category. Do you have this experience? Many Thanks! Xin [[alternative HTML version deleted]]

I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error: > lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr) Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, : initial value in 'vmmin' is not

Hi everyone, I am currently using the function optim() to maximize/minimize functions and I would like to see more output of the optimization procedure, in particular the numerical gradient of the parameter vector during each iteration. The documentation of optim() describes that the trace parameter should allow one to trace the progress of the optimization. I use the following command:

Hi, I am looking for some help regarding the use of the mle function. I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that have been defined in the the log-likelihood equation as theta0=theta[1], theta1=theta[2] and theta2=theta[3]. My R code for mle is: mle(Poisson.lik, start=list(theta=c(20,1,1), method="Nelder-Mead", fixed=list(w=w, t1=t1, t2=t2)) But I keep

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

Hi, my name is Marcel R. Lopes. My problem is, I made a code to calculate the estimates of a Cox model with random effects. Used to optimize the R command for this. The estimates were calculated correctly, but the Hessian matrix does not have good values. The same thing was done in SAS and gave good results for the Hessian Matrix. Where is the problem in R? As the Hessian is calculated?. How

Hello, I want to find the roots of an equation in two variables. I am aware of the uniroot function, which can do this for a function with a single variable (as I understand it...) but cannot find a function that does this for an equation with more than one variable. I am looking for something implementing similar to a Newton-Raphson algorithm. Thanks. -- Bill Shipley North American Editor for

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