Sentence examples for mixed model evaluation from inspiring English sources

The mixed model evaluation will be considered for both individual markers (univariate outcomes) and pathway-based multiple markers (multivariate outcomes), of which we propose the latter to be our primary evaluation scheme.

Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis.

For all evaluations, mixed model equations were solved using MiX99, which makes use of the preconditioned conjugate gradient algorithm [ 19].

We present a small example describing the mixed model equations for genetic evaluations and two simulated examples to illustrate the Bayesian variance component estimation.

We did not perform mixed models in this evaluation because we had not enough information about probably influential confounders.

Further, we explore the data of the process analyses with mixed models and the evaluation questionnaires in the EG. Statistical significance is determined at α=0.05.

Using NRM with GIAs results in adding more animal records to a dataset for variance component estimation and in calculating genetic evaluation by the mixed model procedure.

Thirdly, Roberts et al (1999) used the software PAP, and we used SAGE for conducting our analyses, and these two differ with regard to the numerical approximations of likelihood evaluation under the mixed model (i.e., major gene in addition to possible polygenes and environmental effects).

Distribution of data will be assessed using a graphical evaluation of residuals from the linear mixed model and Kolmogorov-Smirnov tests.

Accuracies that are obtained as an output of the genetic evaluation model, i.e. obtained from the mixed model equations in pedigree BLUP, can be biased if pre-selection occurs on for instance MEBV [ 32].

Application of test-day models for the genetic evaluation of dairy populations requires the solution of large mixed model equations.