I am trying to use regression to determine the interaction between a couple of variables while correcting for autocorrelation. Thus far, I have created the code: model <- gls(yvar~xvar1*xvar2, correlation = corARMA (p=2), method = "ML", data = data)
I'm having a difficult time understanding the different correlation structure classes and when to use the correct ones. Also, with regards to "method", I am not sure if REML or ML is the correct option. Thanks to anyone who can give me help with this. I really appreciate it. -- View this message in context: http://r.789695.n4.nabble.com/Times-Series-Data-using-GLS-tp4636026.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.
