Sentence examples for mixed model packages from inspiring English sources

We show that standard regression diagnostics available in common linear mixed model packages can be used to detect and locate inconsistency in trial networks.

This information is part of the standard output of mixed model packages, but is not convenient for routine reporting in case of a larger number of treatments.

From this analysis, we can obtain residual and influence diagnostics [ 18, 19] by standard procedures with most linear mixed model packages.

The REML estimate of M can be computed from a mixed model package by noting that (32) var u ^ = M = G - C 22.

The presentation assumes that the reader has access to a mixed model package using restricted maximum likelihood (REML) to estimate variance components and is familiar with the essentials of the underlying theory [ 8].

More recently, direct estimation enforcing a FA structure has been proposed and suitable algorithms for both restricted maximum likelihood (REML) [ 5, 6, 44, 45] and Bayesian estimation [ 46] have been described, and mixed model software packages available, such as ASReml [ 47] or WOMBAT [ 48], readily accommodate such analyses.

The MCMCglmm (MCMC generalised linear mixed models) package [ 32] was used to fit transformed data (log FEC+1)) to a multitrait animal model to estimate correlations between June and October.

Data were analysed using linear mixed models (package 'nlme') in R (R Core Development Team, 2013), incorporating nested random effects for bird (where appropriate) and natal family to account for the structure within the data.

A fixed effect of herd; and significant covariates meat percentage, slaughter weight and age at slaughter, were estimated using a linear mixed model in software package ASReml2 [ 44] (1).

The Poisson regressions and logistic regressions were implemented with the MCMC Bayesian generalized linear mixed model (mcmcglmm package), when longitudinal data were available, controlling for individual as a random effect.

The MAANOVA mixed model R-package was used to analyze the response for each gene separately, allowing for differences between slides and between arrays within slide (there being eight identical arrays per slide) and assessing for differences between the six different sources of material (five infected farms and the control noninfected mushrooms).