similar to: Metropolis-Hastings in R

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) }

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

Dear all, I have a function to optimize for a set of parameters and want to set a constraint on only one parameter. Here is my function. What I want to do is estimate the parameters of a bivariate normal distribution where the correlation has to be between -1 and 1. Would you please advise how to revise it? ex=function(s,prob,theta1,theta,xa,xb,xc,xd,t,delta) { expo1=

I'm testing some different formulations of pairwise interaction point processes in Spatstat (version 1.11-6) using R 2.5.0 on a Windows platform and I wish to simulate them using the Metropolis-Hastings algorithm implemented with Spatstat. Spatstat utilizes Fortran77 code with the preprocessor RatFor to do the Metropolis-Hastings MCMC, but the Makefile is more complicated than any I have

Hi, here comes my problem, say I have the following functions (example case) #------------------------------------------------------------ function1 <- function (x, theta) {a <- theta[1] ( 1 - exp(-theta[2]) ) * theta[3] ) b <- x * theta[1] / theta[3]^2 return( list( a = a, b = b )) } #----------------------------------------------------------- function2<-function (x, theta) {P

I am having the following error in my function function(theta,reqdIRR) { theta1<-theta[1] theta2<-theta[2] n<-length(reqdIRR) constant<- n*(theta1+theta2) sum1<-lapply(reqdIRR*exp(theta1),FUN = sum) sum2<-lapply(exp(theta2 - reqdIRR*exp(theta1)),FUN = sum) sum = sum1 + sum2 log.fcn = constant - as.numeric(sum) result = - log.fcn return(result) } *error :

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Hi, With the following Sweave minimal file: ---<--------------------cut here---------------start------------------->--- \documentclass{article} \usepackage{Sweave} \begin{document} <<binom-sim>>= thetas <- seq(0, 1, by=0.001) prior <- rep(1, length(thetas)) / length(thetas) lik <- dbinom(1, 1, thetas) lik.p <- prior * lik post <- lik.p / sum(lik.p)

I get the following error: Error in backSpline.npolySpline(sp) : spline must be monotone Has anyone had this error before? any ideas on a workaround? > > 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){ +

Dear R-help, I am trying to estimate a Cox model with nested effects basing on the minimization of the overall AIC; I have two frailties terms, both gamma distributed. There is a error message (theta2 argument misses) and I don?t understand why. I would like to know what I have wrong. Thank you very much for your time. fitM7 <- coxph(Surv(lifespan,censured) ~ south + frailty(id,

Hi, I am trying to determine the MLE of the following function: http://r.789695.n4.nabble.com/file/n3317341/untitled.bmp I have defined both parts of the equation as separate functions and looped over the t and G values to get summations of each part. The lamda function has 3 unknowns which I am trying to determine using MLE bub tin order to try and get the overall function working these

Hi, My name is Laura. I'm a PhD student at Queen's University Belfast and have just started learning R. I was wondering if somebody could help me to see where I am going wrong in my code for estimating the parameters [mu1, mu2, lambda1] of a 2-phase Coxian Distribution. cox2.lik<-function(theta, y){ mu1<-theta[1] mu2<-theta[2] lambda1<-theta[3]

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 R users I'm a new user of R and I have a basic question about the 4-parameter logistic model. According to the information from Pinheiro & Bates the model is: y(x)=theta1+(theta2-theta1)/(1+exp((theta3-x)/theta4)) == y(x)=A+(B-A)/(1+exp((xmid-input)/scal)) from the graph in page 518 of the book of the same authors (mixed models in S) theta 1 corresponds to the horizontal asymptote

Hello all, could you tell where is an example of metropolis hasting? Thank you! Tania Sent from my iPod

Hi, Can anyone help to write a more elegant version of my code? I am sure this can be put into a loop but I am having trouble creating the objects b1,b2,b3,...,etc. b1 <- rigamma(50,1,1) theta1 <- rgamma(50,0.5,(1/b1)) sim1 <- rpois(50,theta1) b2 <- rigamma(50,1,1) theta2 <- rgamma(50,0.5,(1/b2)) sim2 <- rpois(50,theta2) b3 <- rigamma(50,1,1) theta3 <-

Hi all, I want to draw a histgram for each row of a matrix and compare them. However the plot I got does not have the same y range and x range, which makes it difficult to make the comparison. Is there a easy way to fix the x range and y range in a xy plot for several plots, instead of specifying them for each plot. The following is my code for generalizing the matrix and draw the histogram.

I have a Master's student working on a project which involves estimating parameters of a certain model via maximum likelihood, with the maximization being done via optim(). A phenomenon has occurred which I am at a loss to explain. If we use certain pairs of starting values for optim(), it simply returns those values as the ``optimal'' values, although they are definitely not

Hello all, This is the error message that I get. > hyp.res <- nls(log(y)~log(pdf.hyperb(theta,X)), data=dataModel, + start=list(theta=thetaE0), + trace=TRUE) 45.54325 : 0.1000000 1.3862944 -4.5577142 0.0005503 3.728302 : 0.0583857346 0.4757772859 -4.9156128701 0.0005563154 1.584317 : 0.0194149477 0.3444648833 -4.9365149150 0.0004105426 1.569333 :

Hi guys. I have a dataset with 4 columns. In the first and second column I have the same qualitative variable referred to different teams of people. There are 10 teams in total and they compete against each other to perform a certain task whose result is stored in the third column for the team recorded in the first column, and in the fourth column for the team in the second column. For example,