Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly.
So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum likelihood" for parameter estimation. ML has the advantage, that likelihood ratios can be computed even with changes in model structure. In addition, ML works with the stepAIC function from MASS-library which I am currently using for model-building. I am wondering, why REML is the default setting in NLME and therefore somehow preferred by the authors. What is the main reason to use REML? Maybe I am lacking here statistical knowledge. Any hint, including reference to literature would be very helpful. Best regards, Klaus Thul ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
