"Ronaldo Reis Jr." <[EMAIL PROTECTED]> writes: > Hi, > > I know that is not possible make a stepwise procedure using REML in R, I can > use ML for this. > > For nested design it may be very dangerous due the difference in > variance structure, mainly in a splitplot design. ML make > significative variables that REML dont make.
It would be good to quote an example that shows this. I'm not sure that this occurs in general. > I read an article that is made a stepwise procedure using GENSTAT. > > from article: > "Terms were dropped from a model in a stepwise procedure by assessing the > change in deviance between the full model and the submodel." > > All are made using REML. > > It is possible?! I dont know GENSTAT. You would need to be more specific about how the comparisons are made. I assume that you plan to keep the random effects structure constant and compare two nested models that differ only in the fixed effects terms. I can think of four ways of doing this: 1) Use the F-test obtained by fitting the full model and conditioning on the estimates of the random effects parameters. This is what the anova function applied to an model fit by lme gives. 2) Fit both models and compare the values of the REML criterion in a likelihood ratio test. 3) Fit both models by REML and compare the values of the log-likelihood (i.e. the ML criterion) in a likelihood ratio test. You can obtain that value with logLik(fm, REML=FALSE) if fm is your fitted model. 4)Fit both models and evaluate the REML criterion for the full model at the two sets of estimates. Compare these values in a likelihood ratio test. I feel that 1) is appropriate, 2) is inappropriate, 3) may be appropriate and 4) looks interesting. 4) is based on recent work by Greg Reinsel. In some simulations reported in chapter 3 of Pinheiro and Bates (2000) 3) fared badly compared to 1). ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
