Statistical analysis of data from repeated measures experiments with missing factor combinations encounters multiple complications. Data from asynchronous cyclic drought experiments incorporate unequal numbers of drought cycles for different sources and provide an example of data both with repeated measures and missing factor combinations. Repeated measures data are problematic because typical analyses with PROC GLM do not allow the researcher to compare candidate covariance structures. In contrast, PROC MIXED allows comparison of covariance structures and several options for modeling serial correlation and variance heterogeneity. When there are missing factor combinations, the cross-classified model traditionally used for synchronized trials is inappropriate. For asynchronous data, some least squares means estimates for treatment and source main effects, and treatment by source interaction effects are inestimable. The objectives of this paper were to use an asynchronous drought cycle data set to 1) model an appropriate covariance structure using mixed models, and 2) compare the cross-classified fixed effects model to drought cycle nested within source models. We used a data set of midday water potential measurements taken during a cyclic drought study of 15 half-siblings of bigtooth maples (Acer grandidentatum Nutt.) indigenous to Arizona, New Mexico, Texas, and Utah. Data were analyzed using SAS PROC MIXED software. Information criteria lead to the selection of a model incorporating separate compound symmetric covariance structures for the two irrigation treatment groups. When using nested models in the fixed portion of the model, there are no missing factors because drought cycle is not treated as a crossed experimental factor. Nested models provided meaningful F tests and estimated all the least squares means, but the cross-classified model did not. Furthermore, the nested models adequately compared the treatment effect of sources subjected to asynchronous drought events. We conclude that researchers wishing to analyze data from asynchronous drought trials must consider using mixed models with nested fixed effects.
Dawn M. VanLeeuwen, Rolston St. Hilaire, and Emad Y. Bsoul
Thomas H. Spreen, Jean-Paul Baldwin, and Stephen H. Futch
dummy variable that indicates the presence of HLB in period t. Lagged new plantings are included to account for the presence of probable serial correlation occurrences within time series models. Disaggregating new plantings into early-mid and Valencia
Luisa Martelloni, Lisa Caturegli, Christian Frasconi, Monica Gaetani, Nicola Grossi, Simone Magni, Andrea Peruzzi, Michel Pirchio, Michele Raffaelli, Marco Volterrani, and Marco Fontanelli
not significantly different from zero, the Breusch–Pagan for homoscedasticity, and the Durbin–Watson test for serial correlation. The weed density data followed a Poisson distribution (count data) and were modeled in a generalized linear mixed model