Em Ter, 2008-10-14 às 17:13 +0200, Arnau Mir Torres escreveu: > Hello. > > I need to know how can R compute AIC when I study a regression model? > For example, if I use these data: > growth tannin > 1 12 0 > 2 10 1 > 3 8 2 > 4 11 3 > 5 6 4 > 6 7 5 > 7 2 6 > 8 3 7 > 9 3 8 > and I do > model <- lm (growth ~ tannin) > AIC(model) > > R responses: > 38.75990 > > I know the following formula to compute AIC: > AIC= -2*log-likelihood + 2*(p+1) > > In my example, it would be: > AIC=-2*log-likelihood + 2*2 > but I don't know how R computes log-likelihood: > > logLik(model) > 'log Lik.' -16.37995 (df=3)
Arnau, LogLik= -16.37995 AIC= -2*log-likelihood + 2*(p+1) AIC=-2*-16.37995 + 2*(p+1) AIC= 32.7599+2*(p+1) # # this is very important the model have two # parameter, because sigma is a parameter to. # so # AIC= 32.7599+2*(2+1) AIC= 32.7599+6 AIC= 38.7599 -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil ______________________________________________ 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.
