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Andre Luiz Biscaia Ribeiro da Silva, Joara Secchi Candian, Elizanilda Ramalho do Rego, Timothy Coolong, and Bhabesh Dutta

. All analyses had the heterogeneous compound symmetry as the covariance structure with the smallest Akaike’s information. Least square means comparisons were performed using the Tukey-Kramer adjusted probability value of 0.05, and means were portioned

Open access

Andre Luiz Biscaia Ribeiro da Silva, Joara Secchi Candian, Lincoln Zotarelli, Timothy Coolong, and Christian Christensen

treated as a random effect, whereas year was treated as a repeated measurement. The covariance structure used according to the smallest Akaike’s information criterion was the heterogeneous compound symmetry. Least square means comparisons were performed

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Stefano Fiorio, Stefano Macolino, and Bernd Leinauer

seeding date and turfgrass cultivars on turfgrass quality and NDVI, data were subjected to a repeated-measures analysis (compound symmetry covariance structure) using Proc mixed of SAS (version 9.2; SAS Institute, Cary, NC). Fisher’s protected least

Free access

Yushan Duan, Thomas W. Walters, and Timothy W. Miller

control were analyzed using a mixed-effect model for repeated experiment procedure. An AR(1) covariance structure was used as the optimum for repeated measures as based on the minimum AICc value criteria. SAS 9.2 software (SAS Institute, Inc., Cary, NC

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Verónica Raga, Guillermo P. Bernet, Emilio A. Carbonell, and Maria J. Asins

-order autoregressive covariance structure between measurements taken from the same tree over the years. Pearson’s correlation analyses between fruit yield parameters and all evaluated traits were studied under control and salinity conditions using the 13 apomictic

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Emad Bsoul, Rolston St. Hilaire, and Dawn M. VanLeeuwen

, were evaluated using a mixed model ( VanLeeuwen et al., 2006 ). Differences in least-squares means were assessed at P ≤ 0.05 using the PDIFF option. Analyses included repeated measures (drought cycles) using compound symmetric (CS) covariance

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Warren E. Copes and Eugene K. Blythe

as a continuous variable in the model statement. Heterogeneous variances were fit using the Toeplitz covariance structure. Replication was defined as an independent random effect using the SUBJECT option in a REPEATED statement. Results In

Open access

Jacqueline Cormier, Robert Heyduck, Steven Guldan, Shengrui Yao, Dawn VanLeeuwen, and Ivette Guzman

of the subplot and averaged. Data analysis. For the kale and spinach, plot totals were analyzed by year using a mixed model with species as the fixed effect and fitting an unstructured covariance with blocks. The fitted covariance structure accounted

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Steven J. McKay, James M. Bradeen, and James J. Luby

means of the “simulate” function contained within the lme4 package for R ( Bates, 2007 ), incorporating the variance–covariance structure of the fitted linear model and reflecting the unbalanced nature of the original data set. Each simulated data set

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Laurence Gendron, Guillaume Létourneau, Julien Cormier, Claire Depardieu, Carole Boily, Raymond Levallois, and Jean Caron

) with repeated measures using PROC MIXED to assess the impact of treatments (T), date (D) of harvest, and T × D interaction on both marketable yields and gross revenues. We used blocks as the random effect. We tested different covariance structures for