Titlebook: Genetic Data Analysis for Plant and Animal Breeding; Fikret Isik,James Holland,Christian Maltecca Book 2017 Springer International Publish

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書目名稱Genetic Data Analysis for Plant and Animal Breeding影響因子(影響力)




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書目名稱Genetic Data Analysis for Plant and Animal Breeding被引頻次




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書目名稱Genetic Data Analysis for Plant and Animal Breeding讀者反饋




書目名稱Genetic Data Analysis for Plant and Animal Breeding讀者反饋學(xué)科排名

Robust

Abrupt

Tapan K. Sengupta,Swagata Bhaumik familiar with traditional analysis of variance (ANOVA) based on ordinary least squares methods, we first will review the ANOVA and compare ANOVA to mixed models analysis to help introduce this topic. We will show that under certain conditions, results from ANOVA and mixed models analysis are largel

細(xì)胞學(xué)

DOGMA 2003. Report from Denmarkn particular, ASReml makes use of a notation for direct products of matrices to form some complex variance structures. The direct product notation can be applied both to the residual errors from the model (in the ‘. structure’) and to random model factors (in the ‘. structure’). In this chapter we i

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Limpid

Limpid

,Befehle der Befehls-Oberfl?che,n plant and animal breeding programs. When traits are correlated, breeding value predictions from a multivariate model can be more accurate than univariate models. In this chapter we introduce multivariate models for two data sets: a maize inbred line multi-environment trial and pig data with pedigr

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微枝末節(jié)

https://doi.org/10.1007/978-3-8349-6242-3onmental conditions to which a cultivar might be exposed. Multi-environment trials provide information about the adaptability of genotypes to specific environments or to sets of environments. The variance-covariance structures introduced in preceding chapters can be used to model genotype-by-environ

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