Data were analyzed using linear mixed models enabling us to display the effects of both the considered explanatory variables and their interactions.
Multivariate mixed models enabled us to estimate the genetic covariance between expression traits in a variance-component form that has a standard interpretation for any pair in a multivariate set of quantitative traits and has well-known statistical properties (Lynch and Walsh 1998).
Here, we study the landscape-level phenologies of the marshes under different climatic conditions, using Landsat-derived Normalized Difference Vegetation Index (NDVI) records (30 × 30 m2 spatial resolution) and a nonlinear mixed model that enables a quantitative analysis of nonlinear and piecewise functions involving repeated measures.
Besides the induction of correlated observations within the same inspector, the mixed model approach also enables one to specify the covariance structure of the inspection data.
The mixed model analysis will enable inclusion of time varying covariates such as breastfeeding behaviors, depression, and sleep, which may vary between baseline and follow-up for individuals.
This paper presents a hierarchical multiscale approach based on a micro-kinetic model enabling to predict temperature, pressure and species generated during the thermite reaction of Al nanoparticles mixed with CuO nanoparticles.
We chose to analyze the five probe means for each gene, rather than take an overall mean (or median), because our mixed-model approach enabled us to consider all this information in the model rather than discard the variation that may be present among different probes targeting the same gene.
Linear mixed model analyses were used, enabling separation of variance attributable to individual factors and variance attributable to family membership (i.e., family cluster effect).
The linear mixed model handles unbalanced data, enabling all available information from randomised patients to be included, as well as from dropouts.
Mixed-effect logistic regression models enabled the correlations between repeated measures to be taken into account.
