Proposal of a mixed linear and a mixed generalized model for the analysis of an experiment in rumen microbiology

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Magaly Herrera
Juana Galindo
C. Padilla
Caridad W. Guerra
Yolaine Medina
Lucía Sarduy

Abstract

The objective of this study was to propose the mixed generalized and mixed linear models for the analysis of an experiment in rumen microbiology. For developing this research, data from a study developed in the Department of Biophysiological Sciences of the Institute of Animal Science were used. The effect of different origins and/or varieties of Moringa oleifera on the ruminal microbial population was evaluated. A completely randomized design was applied, associated with a simple variance analysis model, with a 6x3 factorial arrangement. Eighteen treatments were established, which were related to the origin or varieties of Moringa oleifera and three times, with six repetitions each. Theoretical assumptions of the analysis of variance for the original variables homogeneity and normality of errors were verified. When they were not fulfilled, the mixed generalized linear model was used as an alternative to the analysis, and if not, the mixed linear model, with the help of GLIMMIX and MIXED procedure of SAS. In both models, treatment, hour and interaction treatment per hour were considered as fixed effects, and nested repetition within hours was considered as random. Results showed that the mean square of the error was low, when mixed procedures were used. Standard errors also decreased, which contributes to greater precision in results. From this perspective, these models are proposed for the analysis of related variables and counting experiments in the ruminal microbial population.
Key words: GLIMMIX, analysis of variance assumptions, nested
effect

Article Details

How to Cite
Herrera, M., Galindo, J., Padilla, C., Guerra, C. W., Medina, Y., & Sarduy, L. (2020). Proposal of a mixed linear and a mixed generalized model for the analysis of an experiment in rumen microbiology. Cuban Journal of Agricultural Science, 54(2). Retrieved from https://cjascience.com/index.php/CJAS/article/view/954
Section
Biomathematics

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