search for: log_likelihood

..._________________ De : Bomee Park <bombom@stanford.edu> À : r-help@r-project.org Envoyé le : Jeudi, 29 Janvier 2009, 4h01mn 56s Objet : [R] standard error of logit parameters Hi everyone. I am now estimating the parameters for a logit model, and trying to get the estimates by laximizing the log_likelihood. The nlm function works nicely for maximizing the -(log_likelihood) and returns the parameter estimates that minimize the static, and the gradients also, but don't have any clue how I can get the standard error for the parameters. Any help will be greatly appreciated. Thanks. ________________...

...rying 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) { aic <- (-2 * mll) + (k * 2) aic } mlogl_process <- f...

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems