Sentence examples for mixed models use from inspiring English sources

Linear mixed models use all available longitudinal data while adjusting for within-patient correlation.

Linear mixed models use all data available at each time point; thus missing data imputation was not undertaken.

Furthermore, in the secondary analyses the mixed models use all the available values of the residents and do not exclude residents because of partially incomplete values.

Generalised linear mixed models use quasi-likelihoods rather than likelihoods and so log-likelihood values were not available for binary models.

These methods follow the broad spectrum of genetic approaches for dissecting complex traits, from general mixed models, use of covariances between relatives and identification of parent of origin effects in phenotype inheritance for traits without genotypic information available; to the marker-based approaches of linkage mapping, association studies and QTL mapping.

The strength of relationships between latitude and genetic diversity (He and Na), physiological versatility, resilience and heatwave impact were tested with linear mixed models using R v.3.2.2.

We tested for diversity patterns related to each room's categorical (room type, floor level, and floor type), and continuous variables (number of windows and doors leading to the outside) with generalized linear mixed models using the R package lme4 28.

Linear mixed models, using a heterogeneous first-order autoregressive covariance structure and restricted maximum-likelihood estimation, were performed to assess the effects of ketamine versus placebo on SHAPS scores over the 4-week period in this crossover design.

To correct for the population structure in these Arabidopsis thaliana accessions, single-trait genome scan was performed based on linear mixed models, using the polygenic and mmscore procedure in GenABEL.

Data were analyzed in linear mixed models using repeated measures within herd where MFP was considered the outcome variable.

All models were fitted as nonlinear mixed models using the SAS macro NLINMIX.