JSM 2011 Online Program

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Abstract Details

Activity Number: 359
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #303335
Title: Analysis of Complex Multivariate Interactions Using Generalized Linear Latent and Mixed Modeling
Author(s): Muhammad Yaseen*+ and Kent M. Eskridge and Jose Crossa
Companies: University of Nebraska at Lincoln and University of Nebraska and International Maize and Wheat Improvement Center
Address: , Lincoln, NE, 68583,
Keywords: Multivariate Interactions ; GLLAMM ; AMMI ; Three-way Sites Regression
Abstract:

Modeling complex interactions involving multiple response variables is a difficult problem in multivariate data analysis. Response variables are often correlated with each other and are influenced by factor main effects, interactions and covariates. When analyzing these types of data, the covariances and causal structure among the response variables should be taken into account. The generalized linear latent and mixed models (GLLAMMs) combine features of generalized linear mixed models and structural equation models. GLLAMM can be used to incorporate both random and fixed effects as well as model the causal structure among response variables and is more general than other methods such as AMMI and three-way sites regression which can handle only fixed effects and can't include causal structure. The use of GLLAMM to model complex multivariate interactions is illustrated with a duram wheat dataset containing seven cultivars tested over six years with four agronomic response variables and a number of covariates. The GLLAMM approach provided biologically more meaningful insight into the multivariate genotype-by-environment interactions than was possible with other methods.


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