Sentence examples for mixed model variance from inspiring English sources

We found that the random effects explained almost 10% of the mixed model variance.

BrdU and fusion index data were analyzed by mixed model variance analysis, using treatment (P311 and GFP control) as fixed effects with experiments and replicate as random effects.

We applied a mixed model (variance components analysis) to assess the proportion of total variation due to variation among subjects, hybridizations, labels, and chips, among the randomly selected within-subject replicates (n = 19).

Fibre size, fibre density, real-time PCR data for PCS (Fig. 3 to 5) were analyzed by mixed model variance analysis using muscle type (ST, LD, and G), disease status (normal, PCS) as fixed effects with litters and replicate as random effects.

A linear mixed model (variance component model), with time and the interaction of time and group as fixed effects and time as random effect intercept and slope, was used to compare WOMAC scores between the exercise therapy group and the control group over the 29-month follow-up period.

To evaluate between group differences, a linear mixed model (variance component structure with time and time*group as fixed effects and time as random effect intercept and slope) was used (IBM® SPSS® Statistics, version 20.0 (IBM Corp., Somers, New York, USA).

aThe closest SNP marker to the QTL peak position, also used in multiple QTL mapping, and in mixed model and variance components analyses.

Polygenic effects explained by available SNPs can be modeled by the linear mixed model whose variance covariance matrix is parameterized with the genetic relationship matrix [ 25– 27], and BLUP can be used to predict the disease risk by genetic effects.

As the full pedigree of the population is known, this was accounted for using a mixed model with variance components to account for each stratum (between funnels and between outcrossed plants within funnels).

Because the full pedigree of the population is known, this was accounted for using a mixed model with variance components to account for each stratum (between funnels and between outcrossed plants within funnels).

Briefly, univariate mixed models of variance were fitted for each expression trait, using as fixed effects slaughter batch (7 levels), hybridization batch (2 levels) and spotting batch (2 levels).