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A way to apply the result to non-linear mixed models is described.
The h-likelihood approach to multivariate generalized linear mixed models is extended.
Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs).
For the purpose of comparison, additional generalized additive models (gam) were parameterized, because the prediction accuracy of the mean population model of mixed models is normally a little lower.
The purpose of linear mixed models is to factor in the correlations between predictors.
The calculation of the generalised heritability in complex linear mixed models is not straight-forward [ 27].
Similar(45)
Generalized linear mixed models were constructed with random effects.
Generalised linear mixed models were used to analyse performance data.
Linear mixed models were fitted using ASREML.
Mixed models were used to analyse immunogenicity parameters and laboratory tests among groups over time.
Subsequently, linear mixed models were used to compare the virological parameters between the patient groups.
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