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The model includes a fixed regression of phenotypes using SNP genotypes as a measure of the SNP effects.
In the future, as more TD data become available electronically, the use of a fixed regression TDM could be a viable option.
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Because of the large size of the data, all fixed regression parameters are very precisely estimated with very small standard errors.
For the fixed regression parameters, a suitable choice is the diffuse prior, i.e., p ∝ const, but a weakly informative Gaussian prior is also possible.
For the fixed regression parameters, a suitable choice is the diffuse prior, that is, p∝const, but a weakly informative Gaussian prior is also possible.
For the fixed regression parameters, α, a suitable choice is the diffuse prior, i.e p∝constant.
According to Hardin and Hilbe (2003), when explicitly modeling the source of heterogeneity in the logistic regression with random effects, the fixed regression parameters have an interpretation for individuals, which is subject specific [ 30].
The fixed regression coefficients β1 and β2 were assigned a vague Gaussian prior.
When modeling explicitly the source of heterogeneity in the logistic regression with random effects, the fixed regression parameters should be interpreted as effects of covariates on a typical subject in the study [ 30, 44].
The number of simultaneously estimated parameters was 36 (2 × 13 period factors and 2 × 5 fixed regression coefficients).
β h is the coefficient of linear fixed regression on age at harvest (AGE h ) within the hth environment.
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