Christophe LOOTS <Christophe.Loots <at> ifremer.fr> writes: > > Hi, > > I've two fitted models, one binomial model with presence-absence data > that predicts probability of presence and one gaussian model (normal or > log-normal abundances). > > I would like to evaluate these models not on their capability of > adjustment but on their capability of prediction by calculating the > (log)likelihood between predicted and observed values for each type of > model. > > I found the following formula for Bernouilli model : > > -2 log lik = -2 sum (y*log phat + (1-y)*log(1-phat) ), with "phat" is > the probaility (between 0 and 1) and "y" is the observed values (0 or 1). > > 1) Is anybody can tell me if this formula is statistically true?
This looks correct. > 2) Can someone tell me what is the formula of the likelihood between > observed and predicted values for a gaussian model ? > -2 L = sum( (x_i - mu_i)^2)/sigma^2 - 2*n*log(sigma) + C assuming independence and equal variances: but don't trust my algebra, see ?dnorm and take the log of the likelihood shown there for yourself. You're reinventing the wheel a bit here: -2*sum(dbinom(y,prob=phat,size=1,log=TRUE)) and -2*sum(dnorm(x,mean=mu,sd=sigma,log=TRUE)) will do what you want. Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
