Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa.
For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno <- gamlss(X~1,family=LNO) GAMLSS-RS iteration 1: Global Deviance = 2185.763 GAMLSS-RS iteration 2: Global Deviance = 2185.763 Gexp$aic [1] 2039.825 Glno$aic [1] 2189.763 Can anybody shed light on why these results would occur? cheers, RdR -- View this message in context: http://r.789695.n4.nabble.com/gamlss-results-for-EXP-and-LNO-seem-to-have-reversed-AIC-scores-tp4409754p4409754.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
