search for: mlogl

Hi, I tried to compute maximum likelihood under gamma distribution, using nlm function. The code is this: __BEGIN__ vsamples<- c(103.9, 88.5, 242.9, 206.6, 175.7, 164.4) mlogl <- function(alpha, x) { if (length(alpha) > 1) stop("alpha must be scalar") if (alpha <= 0) stop("alpha must be positive") return(- sum(dgamma(x, shape = alpha, log = TRUE))) } mlogl_out <- nlm(mlogl, mean(vsamples),vsamples=vsamples) print(mlogl_out)...

As part of carrying out a complicated maximum likelihood estimation, I am trying to learn to program likelihoods in R. I started with a simple probit model but am unable to get the code to work. Any help or suggestions are most welcome. I give my code below: ************************************ mlogl <- function(mu, y, X) { n <- nrow(X) zeta <- X%*%mu llik <- 0 for (i in 1:n) { if (y[i]==1) llik <- llik + log(pnorm(zeta[i,], mean=0, sd=1)) else llik <- llik + log(1-pnorm(zeta[i,], mean=0, sd=1)) } return(-llik) } women <- read.table("~/R/Examples/W...

Hi, I have a problem in programming for bootstrapping. I don't know why it show the error message. Please see my code below: #'st' is my original dataset. #functions of 'fml.mlogl','pcopula.fam4','ltd','invltd' are already defined boot.OR<-function(data,i) { E=data[i,] ml1<-glm(c_VAsex90_bf ~ trt,family=binomial,data=E) ml2<-glm(c_VAsex90_bm ~ trt,family=binomial,data=E) marg.covariates<-cbind(rep(1,length(E$trt)),E$trt) dep.covariate...

Hello! I need to perform maximum likelihood estimation on R, but I am not sure which command to use. I searched on google, and found an example using the function mlogl, but I couldn't find the package on R. Is there such function? Or how should i perform my mle? Thank you very much. -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-tf4103791.html#a11670424 Sent from the R help mailing list archive at Nabble.com.

Dear all, I am trying to model number of samples from a given series. The series are modelled according Gamma function. In order to estimate the # samples, I use BIC/AIC with MLE (computed from dgamma function). Here is the code I have. __BEGIN__ mlogl <- function( x_func, theta_func, samp) { # computing log_likelihood return( - sum(dgamma(samp, shape = x_func, scale=theta_func, log = TRUE))) } find_bic <- function(mll,smpl,k) { bic <- (-2 * mll) + (k * log(length(smpl))) bic } find_aic <- function(mll,smpl,k) {...

Dear all, I'm using igraph for some analysis about the network I have. I have a question about the function "power.law.fit". I wonder if there is any test for checking whether the "power.law.fit" is good for the input, i.e., under which situation, could we use this function to get a reliable result. I'm afraid even I input a random graph without any property of

Dear list, Does anyone know how to fit the power law distribution? I have the empirical distribution and would like to check whether it fits power law (with the power estimated from the data). Any hints are appreciated Best regards Galina [[alternative HTML version deleted]]