Abstract
A major advantage of PROC MIXED for repeated measures data is that one could choose from many different correlated error models. However, MIXED uses default starting values that may cause difficulty obtaining REML estimates of the covariance parameters for several of the models available. This can take the form of excessively long run times or even failure to converge. We have written a program to obtain initial covariance parameter estimates that result in greatly improved performance of the REML algorithm. We will use two covariance models frequently of interest in animal health experiments, the first-order ante-dependence model [ANTE(l)] and the Toeplitz model with heterogeneous variances [TOEPH], to illustrate the use of our procedure.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Recknor, J. C. and Stroup, W. W.
(1999).
"STARTING VALUES FOR PROC MIXED WITH REPEATED MEASURES DATA,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1269
STARTING VALUES FOR PROC MIXED WITH REPEATED MEASURES DATA
A major advantage of PROC MIXED for repeated measures data is that one could choose from many different correlated error models. However, MIXED uses default starting values that may cause difficulty obtaining REML estimates of the covariance parameters for several of the models available. This can take the form of excessively long run times or even failure to converge. We have written a program to obtain initial covariance parameter estimates that result in greatly improved performance of the REML algorithm. We will use two covariance models frequently of interest in animal health experiments, the first-order ante-dependence model [ANTE(l)] and the Toeplitz model with heterogeneous variances [TOEPH], to illustrate the use of our procedure.
