similar to: MLE

Hi I have been having issue with a ML estimator for Jump diffusion process but know I am get little error I didn't notice before like I am try to create a vector > #GBMPJ MLE Combined Ph 1 LR > # > n<-length(combinedlrph1) > j<-c(1,2,3,4,5,6,7,8,9,10) Error in c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) : unused argument(s) (3, 4, 5, 6, 7, 8, 9, 10) >

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I

A newbie question: I'm trying to decide whether to run a maximum likelihood estimation in R or Stata and am wondering if the R mle routine is reasonably robust. I'm fairly certain that, with this data, in Stata I would get a lot of complaints about non-concave functions and unproductive steps attempted, but would eventually have a successful ML estimate. I believe that, with the

Hi, I'm not sure of the correct nomenclature or function for what I'm trying to do. I'm interested in calculated a logistic regression on a binary dependent variable (True,False). There are a few ways to easily do this in R. Both SVM and GLM work easily. The part that I want to add is "group wise" awareness. So that the algorithm computes the coefficients to maximize

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Hi, I have been using maxLik to do some MLE of Geometric Brownian Motion Process and everything has been going fine, but know I have tried to do it with jumps. I have create a vector of jumps and then added this into my log-likelihood equation, know I am getting a message: NA in the initial gradient My codes is hear # n<-length(combinedlr) j<-c(1,2,3,4,5,6,7,8,9,10)

Dear Colleagues, Hope you will be fine. I am student of Ph.D and doing some work on distribution. I developed a new distribution and having some problems in estimating their parameters by MLE. I used R-program and  used "maxLik" function (maxLik: A Package for Maximum Likelihood Estimation in R) But there is some problem, it is not estimated the parameters properly. I also write an

Hi All I have to run multiple stimations and to compute Likelihhod ratio. If I compute ls function with coef and summary I can extract "outputs" that I need. I am not able to find something similar to log liklihood.... Can you pease tell me running a ls function x on y how to extract if posible LR statitic or Likelihood or Log likelihood. Many thanks in advance. If you send me

Apologies up front for failing to find related posts... Am I overlooking a way to get ''zfs send -i fs at 0 fs at 1 | zfs receive -n -v ...'' to show the contents of the stream? I''m looking for the equivalent of ufsdump 1f - fs ... | ufsrestore tv - . I''m hoping that this might be a faster way than using ''find fs -newer ...'' to learn

I have this maximum liklihood estimate problem i need to find the roots of the following: [sum (from i=1 to n) ] ((2(x[i]-parameter)/(1+(x[i]-parameter)^2))=0 given to me is the x vector which has length 100 how would I find the roots using R? I have 2 thoughts...... 1 is using a grid search ... eg. brute force, just choosing a whole bunch of different values for my parameter .... such as

Greetings! I am looking for advice regarding the best way to compare GAMMs. I know other model outputs return enough information for R's AIC, ANOVA, etc. commands to function, but this is not the case with GAMM unless one specifies the gam or lme portion. I know these parts of the gamm contain items that will facilitate comparisons between gamms. Is it correct to simply use these values

Hi all, People watching the git commits might have noticed that I have been fixing a number of issues around undefined behaviour. Why you ask? * Some forms of undefined behaviour have potential for security exploits. * Compiler writers are free to replace anything which invokes UB with a NOP or even, nothing at all. * Having large numbers of UB warnings makes it difficult (or rather time

Hi, I'm using maxlik with functions specified (L, his gradient & hessian). Now I would like determine some robust standard errors of my estimators. So I 'm try to use vcovHC, or hccm or robcov for example but in use one of them with my result of maxlik, I've a the following error message : Erreur dans terms.default(object) : no terms component Is there some attributes

I'm having trouble with adapt. I'm trying to use it in a Bayesian setting, to integrate the posterior distribution, and to find posterior means. I tried using the following script, and things went ok: data = rnorm(100,0.2,1.1) data = c(data,rnorm(10,3,1)) data = data[abs(data)<2*sd(data)] prior = function(x){ dgamma(x[2],shape=2,scale=1)*dnorm(x[1],0,.5) } liklihood =

>From: Sundar Dorai-Raj <sundar.dorai-raj at PDF.COM> >Reply-To: sundar.dorai-raj at PDF.COM >To: Zhen Pang <nusbj at hotmail.com> >CC: r-help at stat.math.ethz.ch >Subject: Vectorising and loop (was Re: [R] optim "a log-likelihood >function") >Date: Wed, 29 Sep 2004 18:21:17 -0700 > > > >Zhen Pang wrote: > >> >>I also use

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

Oh Hi Arne, You may recall we visited with this before. I do not believe the problem is algorithm specific. The algorithms I use the most often are BFGS and BHHH (or maxBFGS and maxBHHH). For simple econometric models such as probit, Tobit, and evening sample selection models, old and new versions of R work equally well (I write my own programs and do not use ones from AER or sampleSekection).

Hi Steven Which optimisation algorithms in maxLik work better under R-3.0.3 than under the current version of R? /Arne On Thu, 8 Oct 2020 at 21:05, Steven Yen <styen at ntu.edu.tw> wrote: > > Hmm. You raised an interesting point. Actually I am not having problems with aod per se?-it is just a supporting package I need while using old R. The essential package I need, maxLik, simply

Hello, First time poster here so let me know if you need any more information. I am trying to run an ordered probit with maximum likelihood model in R with a very simple model (model <- econ3 ~ partyid). Everything looks ok until i try to run the optim() command and that's when I get " Error in rep(1, nrow(x)) : invalid 'times' argument". I had to adapt the code from a 4