Sentence examples for mixed linear modeling from inspiring English sources

Mixed linear modeling was performed with each sheep treated as a random effect.

We began with a polygenic approach, employing mixed linear modeling to investigate the overall contribution of mitochondrial genetic variation on trait variance.

To assess change over time of QoL dimensions in each groups (worsened or non-worsened groups), mixed linear modeling was performed using unstructured covariance matrix after adjusted for covariates: gender, age, marital status, employment status, education level, and disease duration.

Analysis and reporting In the 34 scientific publications, various analytical approaches were used (Appendix 3, see Supplementary data, Table 4), varying from descriptive statistics, all studies, to multivariate techniques such as mixed linear modeling (Nerland et al. 2014, Robinson et al. 2013) and propensity modeling (Kasliwal et al. 2012, Adogwa et al. 2014).

We will use mixed linear model analysis (Proc Mixed in SAS® 8) to quantify these differences.

The mixed linear model uses both fixed and random effects which incorporates kinship among the individuals.

Tassel 3.0 software and the MLM (mixed linear model) were used in the analysis.

The primary outcome was compared using a mixed linear model, adjusting for the period effect.

The mixed linear model (MLM), which accounts for population structure was used for GWA mapping.

A mixed linear model tested the effect of soup portion size on intake.

Mixed linear models were constructed for each of the SF-36 summary scores.