The variable Gender requests a different intercept for the girls and boys, Age models an overall linear growth trend, and Gender* Age makes the slopes different over time. The fixed effects are then listed after the equal sign. The MODEL statement first lists the dependent variable Y. The COVTEST option requests asymptotic tests of all the covariance parameters. To follow Jennrich and Schluchter, this example uses maximum likelihood ( METHOD=ML) instead of the default REML to estimate the unknown covariance parameters. The PROC MIXED statements to fit an unstructured variance matrix (their Model 2) are as follows: Jennrich and Schluchter present results for the Pothoff and Roy data from various covariance structures. See Jennrich and Schluchter (1986), Louis (1988), Crowder and Hand (1990), Diggle, Liang, and Zeger (1994), and Everitt (1995) for overviews of this approach to repeated measures.

The data are assumed to be Gaussian, and their likelihood is maximized to estimate the model parameters. The analysis strategy employs a linear growth curve model for the boys and girls as well as a variance-covariance model that incorporates correlations for all of the observations arising from the same person. Some of the observations are suspect (for example, the third observation for person 20) however, all of the data are used here for comparison purposes. The following data are from Pothoff and Roy (1964) and consist of growth measurements for 11 girls and 16 boys at ages 8, 10, 12, and 14.

Writing sas code for repeated measures