On Wed, Jan 28, 2009 at 9:51 AM, Dr Carbon <drcar...@gmail.com> wrote: > 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) >
I think this is the way to do this? b0 <- coef(fm1)[1] b1 <- coef(fm1)[2] p1 <- intervals(fm1)$corStruct[2] y[i] = b0 + p1*y[i-1] + b1*x[i] ______________________________________________ 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.
