
Hello there fellow R users, 

Im trying to fit a gls model to data which has serial correlation in the
errors e(t)=p*e(t-1). 
However I dont seem to be having much luck in erradicating the
autocorrelation in the residuals. 

I have created the following example.

library(nlme)
x<-rnorm(100)
y<-3+2*x
y<-y+arima.sim(100,model=list(ar=(0.6)))+rnorm(100,0,0.2)
#Create a data set with first order serial correlation in the residuals.



my.mat<-as.data.frame(cbind(y,x))
acf(lm(y~x,my.mat)$residuals)  #fit a linear model and observe the
residuals.


acf(as.numeric(gls(y~x,my.mat,correlation = corAR1())$residuals));  # fit a
gls model with correlated error terms and observe autocorrelation of
residuals


Further more if I use time series fitting I get a different result. 


library(ts)
acf(arima(as.ts(y),xreg=x,order=c(1,0,0))$residuals) # fit a time series
model and observe the residuals


I must be doing something wrong. 

Am I using the correct correlation structure (corAr1).


Thanks in advance,

Wayne


Dr Wayne R. Jones
Senior Statistician / Research Analyst
KSS Limited
St James's Buildings
79 Oxford Street
Manchester M1 6SS
Tel: +44(0)161 609 4084
Mob: +44(0)7810 523 713




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