I attempting to model the relationship between water temperature and air temperature. The seasonal component of the temperature time series has been modeled using a sinusoidal function, leaving the air and water temperature residuals. I want to model the relationship with
M5<- gls(Water ~ Air +Air1 +Air2, correlation = corCAR1(form =~ Date)) Where Water is the water temperature residual, Air is the air temperature residuals at 1 and 2 day lags. I have included an autocorrelation structure that takes into account the fact that the water temperature were taken at irregular spaced intervals. I would like to test whether the time series is stationary, I found a blog post that used the following graphical methods and tests (Cent_Water is the water temperature centered by subtracting the mean value) Acf(Cent_Water) Pacf(Cent_Water) Box.test(Cent_Water, lag=20, type="Ljung-Box") adf.test(Cent_Water, alternative="stationary") kpss.test(Cent_Water) Are these methods useable with irregular spaced data as I believe it is not possible to use Acf? Any suggestions would be greatly appreciated Best wishes Tom ______________________________________________ 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.
