How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure included.
Thanks in advance. -JC PS I am including the package maintainers on this post - does this constitute a maintainer-specific question in r-help etiquette? # example set.seed(123) x <- arima.sim(list(order = c(1,0,0), ar = 0.7), n = 100) y <-x + arima.sim(list(order = c(1,0,0), ar = 0.7), n = 100) x <- c(x) y <- c(y) lm1 <- lm(y~x) ar(residuals(lm1)) # indicates an ar1 model cs1 <- corARMA(p=1) fm1 <- gls(y~x,corr=cs1) summary(fm1) # get fits fits <- predict(fm1) # use coef to get fits fits2 <- coef(fm1)[1] + (coef(fm1)[2] * x) plot(fits,fits2) ______________________________________________ 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.
