Hello, I'm trying to use AIC to choose between 2 models with positive, continuous response variables and different error distributions (specifically a Gamma GLM with log link and a normal linear model for log(y)). I understand that in some cases it may not be possible (or necessary) to discriminate between these two distributions. However, for the normal linear model I noticed a discrepancy between the output of the AIC() function and my calculations done "by hand." This is due to the output from the function logLik.lm(), which does not match my results using the dnorm() function (see simple regression example below).
x <- c(43.22,41.11,76.97,77.67,124.77,110.71,144.46,188.05,171.18, 204.92,221.09,178.21,224.61,286.47,249.92,313.19,332.17,374.35) y <- c(5.18,12.47,15.65,23.42,27.07,34.84,31.03,30.87,40.07,57.36, 47.68,43.40,51.81,55.77,62.59,66.56,74.65,73.54) test.lm <- lm(y~x) y.hat <- fitted(test.lm) sigma <- summary(test.lm)$sigma logLik(test.lm) # `log Lik.' -57.20699 (df=3) sum(dnorm(y, y.hat, sigma, log=T)) # [1] -57.26704 The difference in this simple example is slight, but it is magnified when using my data. My understanding is that it is necessary to use the complete likelihood functions for both the Gamma model and the 'lognormal' model (no constants removed, etc.) in order to accurately compare using AIC. Can someone point out my error, or explain this discrepancy? Thanks in advance! ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help