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This work is licensed under a Creative Commons Attribution 4.0 International License.
This repository has been created to preserve some useful functions for use in plant breeding data analysis using the {lmerTest} and {breedR} R packages. Most of these functions have strong applicability in forest breeding programs, as the datasets used are in most cases from these programs. Please note that these functions are constantly being tested and may be subject to significant changes in the future. Please get in touch for more information.
This function has been designed for use in diagnostic analysis to:
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Check the distribution assumptions of the data (currently normal distribution only )
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Check the skewness and kurtosis
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Perform the statistic normality test: Anderson-Darling, kolmogorov-Smirnov
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Test of heterocedasticityt: Levene test
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Identify discrepant data and outliers
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Box-Cox test
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Graphical analysis
This function allows us to compare different models fitted in the remlf90 function of the {breedR} R package (Muñoz and Sanchez 2020).
The Extract_h2a_breedR has been developed to extract heritability and correlations from models into the remlf90 function of the {breedR} R package.
The Deviance_BreedR function is prompted to be used in the deviance test in models fitted using the remlf90 function from the {breedR} R package.
The BV_BreedR has been developed to extract the breeding values from models into the remlf90 function of the {breedR} R package.
The ExtractLmer has been developed to extract generic parameters from models into the lmer function of the {lmerTest} R package (Kuznetsova, Brockhoff, and Chistensen 2017).
The BV_lmer has been developed to extract the breeding values from models into the lmer function of the {lmerTest} R package.
The Thinning_BreedR function has been developed for use in tree breeding strategies that involve thinning strategies and help to find a balance between breeding and conservation. It also allows us to test and compare different models included in the {breedR} package. More details on the use of this function can be found in the following papers:
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Thinning strategies for Eucalyptus dunnii population: balance between breeding and conservation using spatial variation and competition model (Araujo et al. 2021).
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Conservative or non-conservative strategy to advance breeding generation? A case study in Eucalyptus benthamii using spatial variation and competition model (Araujo et al. 2023).
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Thinning Strategies to Optimize Genetic Gain and Population Size in Eucalyptus pellita Breeding (Silva et al. 2024).
Araujo, Marcio José de, Rinaldo Cesar de Paula, Cristiano Bueno de Moraes, Gustavo Pieroni, and Paulo Henrique Müller da Silva. 2021. "Thinning Strategies for Eucalyptus Dunnii Population: Balance Between Breeding and Conservation Using Spatial Variation and Competition Model." Tree Genetics & Genomes 17 (5). https://doi.org/10.1007/s11295-021-01523-w.
Araujo, Marcio José de, Guilherme Nichele da Rocha, Regiane Abjaud Estopa, Javier Oberschelp, and Paulo Henrique Müller da Silva. 2023. "Conservative or Non-Conservative Strategy to Advance Breeding Generation? A Case Study in Eucalyptus Benthamii Using Spatial Variation and Competition Model." Silvae Genetica 72 (1): 1--10. https://doi.org/10.2478/sg-2023-0001.
Kuznetsova, Alexandra, Per B. Brockhoff, and Rune H. B. Christensen. 2017. "lmerTest Package: Tests in Linear Mixed Effects Models" 82. https://doi.org/10.18637/jss.v082.i13.
Muñoz, Facundo, and Leopoldo Sanchez. 2020. "breedR: Statistical Methods for Forest Genetic Resources Analysts." https://github.com/famuvie/breedR.
Silva, Paulo Henrique Muller, Rocha, Guilherme Nichele, Araujo, Marcio. et al. Thinning Strategies to Optimize Genetic Gain and Population Size in Eucalyptus pellita Breeding. Tree Genetics & Genomes 20, 43 (2024). https://doi.org/10.1007/s11295-024-01674-6