similar to: Maximum likelihood estimation

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

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

I want to calculate Newey West robust standard error using NeweyWest. Comparing the results to what I get in STATA, in order to get the same results in I need to specify "prewhite=0". Can someone explain what this prewhite command means? Thanks [[alternative HTML version deleted]]

The background: I'm trying to fit a Poisson-lognormal distrbutuion to some data. This is a way of modelling species abundances: N ~ Pois(lam) log(lam) ~ N(mu, sigma2) The number of individuals are Poisson distributed with an abundance drawn from a log-normal distrbution. To fit this to data, I need to integrate out lam. In principle, I can do it this way: PLN1 <- function(lam, Count,

Another way to avoid the problem is to not redefine variables that are arguments. E.g., > Su3 <- function(u=100, l=u, mu=0.53, sigma2=4.3^2, verbose) { if (verbose) { print(c(u, l, mu)) } uNormalized <- u/sqrt(sigma2) lNormalized <- l/sqrt(sigma2) muNormalized <- mu/sqrt(sigma2) c(uNormalized, lNormalized, muNormalized) } > Su3(verbose=TRUE)

Hi All, This is not really an R question but a statistical one. If someone could either give me the brief explanation or point me to a reference that might help, I'd appreciate it. I want to estimate the mean of a log-normal distribution, given the (log scale normal) parameters mu and sigma squared (sigma2). I understood this should simply be: exp(mu + sigma2) ... but I the following code

Dear Bill, All makes perfect sense (including the late evaluation). I actually discovered the problem by looking at old code which used your proposed solution. Still I find it strange (and, hnestly, I don?t like R?s behavior in this respect), and I am wondering why u is not being copied to L just before u is assigned a new value. Of course, this would require the R interpreter to track all these

Hello, One way of preventing that is to use ?force. Just put force(l) right after the commented out print and before you change 'u'. Hope this helps, Rui Barradas Citando Matthias Gondan <matthias-gondan at gmx.de>: > Dear R developers, > > sessionInfo() below > > Please have a look at the following two versions of the same function: > > 1. Intended

Dear R developers, sessionInfo() below Please have a look at the following two versions of the same function: 1. Intended behavior: > Su1 = function(u=100, l=u, mu=0.53, sigma2=4.3^2) + { + print(c(u, l, mu)) # here, l is set to u?s value + u = u/sqrt(sigma2) + l = l/sqrt(sigma2) + mu = mu/sqrt(sigma2) + print(c(u, l, mu)) + } > > Su1() [1] 100.00 100.00 0.53 [1]

Dear all, I am stuck on the following problem with integrate(). I have been out of luck using RSiteSearch().. My function is g2<-function(b,theta,xi,yi,sigma2){ xi<-cbind(1,xi) eta<-drop(xi%*%theta) num<-exp((eta + rep(b,length(eta)))*yi) den<- 1 + exp(eta + rep(b,length(eta))) result=(num/den)*exp((-b^2)/sigma2)/sqrt(2*pi*sigma2)

Mathias, If it's any comfort, I appreciated the example; 'expected' behaviour maybe, but a very nice example for staff/student training! S Ellison > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Matthias > Gondan > Sent: 02 September 2017 18:22 > To: r-help at r-project.org > Subject: [R] Strange lazy evaluation of

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,

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

Hi, A question. When I run gelman.diag and gelman.plot with mcmc lists obtained from MCMCregress, the results are following. > post.R <- MCMCregress(Size~Age+Status, data = data, burnin = 5000, mcmc = 100000, + thin = 10, verbose = FALSE, beta.start = NA, sigma2.start = NA, + b0 = 0, B0 = 0, nu = 0.001, delta = 0.001) > post1.R <- MCMCregress(Size~Age+Status, data

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

Hi, I am trying to fit a function of the form: y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5* ( (X-Mu2)/Sigma2 )^2 ) i.e. a mean term (A0) + a difference between two gaussians. The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1. The plot looks like a "Mexican Hat". I had trouble (poor fits) fitting this function to toy data

Greetings, I am using fGarch package to estimate and simulate GARCH models. What I would like to do is to perform Monte Carlo simulation. Unfortunately I cannot figure how to modify the code to achieve this. I use the following code to run a single simulation: spec=garchSpec(model=list(ar= 0.440270860, omega=0.000374365,alpha=0.475446583 , mu=0, beta=0)) sim<-garchSim(spec,

Hello all, I have a large parameter problem with the following very simple likelihood function: fn<-function(param) { x1<-param[1:n] g1<-param[(n+1):(2*n)] beta<-param[(2*n+1):(2*n+k)] sigma2<-param[2*n+k+1]^2 meang1sp<-mean(g1[sp]) mu<-beta%*%matrix(x1,1,n)-(g1[sp]-meang1sp)%*%matrix(g1,1,n) return(sum((ydc-mu)^2)/(2*sigma2) + n*k*log(sqrt(sigma2)) +

I wrote a simple log likelihood (for the ordinary least squares (OLS) model), in two ways. The first works out the likelihood. The second merely calls the first, but after transforming the variance parameter, so as to allow an unconstrained maximisation. So the second suffers a slight cost for one exp() and then it pays the cost of calling the first. I did performance measurement. One would

i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S),