similar to: try to find the MLE of a function

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 am trying to write a lesson on maximum likelihood with Sweave. I get a surprising result with the following code, lec4.Snw: \documentclass[a4paper,12pt]{article} \usepackage[latin1]{inputenc} \title{Maximum likelihood} \author{G伱伓ran Brostr伱伓m} \begin{document} \maketitle <<fig=TRUE>>= ## Simulate Y: n <- 25 Y <- sum(rpois(n, lambda = 1)) Y ## Define minusloglik:

Hi there, I ran the following code: vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption vols.csv" , header=TRUE, sep=",") X<-ts(vols[,2]) #X dcOU<-function(x,t,x0,theta,log=FALSE){ Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) }

Thanks to Gabor for setting me right. My code is as follows. I found it useful for learning optim(), and you might find it similarly useful. I will be most grateful if you can guide me on how to do this better. Should one be using optim() or stats4::mle? set.seed(101) # For replicability # Setup problem X <- cbind(1, runif(100)) theta.true <- c(2,3,1) y <- X

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 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

Hello, I was asked to try the following code on R, gamma.mles function (xx,shape0,rate0) { n<- length(xx) xbar<- mean(xx) logxbar<- mean(log(xx)) theta<-c(shape0,rate0) repeat { theta0<- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)

Hi there, I am trying to run the following code: > dcOU<-function(x,t,x0,theta,log=FALSE){ + Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) + Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) + dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) + } > OU.lik<-function(theta1,theta2,theta3){ + n<-length(X) + dt<-deltat(X) +

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

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

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1

Hi, Assuming I have a time series on which I will perform rolling-window MLE. In other words, if I stand at time t, I'm using points t-L+1 to t for my MLE estimate of parameters at time t (here L is my rolling window width). Next, at t+1, I'll do the same. My question is that is there anyway to avoid performing MLE each time like does the above. My impression is that rolling from point t

Hi: I need to create many variables at one time,how to do this in R? for eg ,X1,X2.......X100? Thanks~ [[alternative HTML version deleted]]

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

Hi everyone, I am looking to do some manual maximum likelihood estimation in R. I have done a lot of work in Stata and so I have been using output comparisons to get a handle on what is happening. I estimated a simple linear model in R with lm() and also my own maximum likelihood program. I then compared the output with Stata. Two things jumped out at me. Firstly, in Stata my coefficient

Hi Guys, it would be great if you could help me with a MLE problem in R. I am trying to evaluate the maximum likelihood estimates of theta = (a1, b1, a2, b2, P) which defines a mixture of a Poisson distribution and two gamma prior distributions (where the Poisson means have a gamma distribution, actually 2 gammas and P is the mixing factor). The likelihood function for theta is L(theta) = Pi,j{P

Does anyone know how to approach R code for the MLE of a geom. distribution? thanks! [[alternative HTML version deleted]]

I need to create 100 variable ,whose name is id.1,id.2~~~~id.100 then I need to let a vector say id<-c(id.1,id.2....id.100) any easy way to do this? thanks a lot~ [[alternative HTML version deleted]]

Dear UseRs, I wrote the following function to use MLE. --------------------------------------------- mlog <- function(theta, nx = 1, nz = 1, dt){ beta <- matrix(theta[1:(nx+1)], ncol = 1) delta <- matrix(theta[(nx+2):(nx+nz+1)], ncol = 1) sigma2 <- theta[nx+nz+2] gamma <- theta[nx+nz+3] y <- as.matrix(dt[, 1], ncol = 1) x <- as.matrix(data.frame(1,