Obtain a correlation matrix for days. Fit a first order autocorrelation structure, with no 'month' effects. Fit a mixed model with random 'month' effects and autocorrelation for days. Compare the auto-correlation with the autocorrelation using weat1.sd2. Compare results with PROC Mixed.
Temperature is plotted verses day of the year for each year, with results overlayed.
The average temperature is computed for each day of the year over the three or four years, and subtracted from the daily temperature. This results in a 'detrended' series of temperatures that are stationnary over time. These models indicate the auto-correlation is 0.66 for temperature. Simulating Mixed Model Data with Autocorrelation. Modify the simulation program to include a random cluster effect and auto-correlation.