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This paper uses a mixed model to deal with different factors, integrate the forecasting results of different factors, and get the final forecasting results.
This package uses a mixed model approach in a Bayesian estimation framework to estimate individual variance components that are constrained by the relatedness matrix (derived from the pedigree), individual effects that are unconstrained by the relatedness matrix and residual error (as well as other possible random effects).
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Variables were analysed using a mixed model.
The data were analyzed using a mixed model analysis.
Data were analyzed using a mixed model procedure.
Data were analyzed with repeated measurements using a mixed model that included the covariate adjustment.
ANOVA was conducted on the experimental data using a mixed model considering the tank effect to be random.
The data were submitted to association mapping using a mixed model with control of structure and kinship.
After variable selection using random forest and univariable mixed logistic regression models, a multivariable analyses using a mixed model with a random effect (center).
Egg location and behaviour patterns were analyzed using a mixed model ANOVA; day within week was the repeated measure.
Differences in cell proliferation were examined using a mixed model nested analysis of variance (ANOVA).
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