Sentence examples for mixed model software from inspiring English sources

The LRT statistic is analytically intractable except in special cases, but readily obtained from standard mixed model software.

Such a correction is not necessary if the variance components for all chromosomes are estimated simultaneously as applied in this study; this is a further advantage besides its straightforward implementation using standard mixed model software packages such as ASReml.

We reassess various reference and non-reference designs for statistical efficiency in terms of standard errors of mean differences, power of test, and robustness using recently developed mixed model software tools.

This model was fitted using the mixed model software lme [ Pinheiro and Bates, 2000] in the R library of statistical software (http://www.r-project.org/).org/

This analysis is easily performed using linear mixed model software with weighting facility by defining the inverse of var e) as weight and fixing the residual variance at unity [ 13].

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.

We fit two general linear mixed models.

All quantifiable plasma samples were included to develop population PK models for sunitinib and SU012662 using Nonlinear Mixed Effect Modeling software (NONMEM; version 7.1.2), following exclusion of plasma samples with inadequate dosing records and those identified to be extreme outliers (eg, 6 < Conditional Weighted Residual(CWRES) < −6).

The regression lines within a group (16 for the STZ group and 17 for the SHAM group) were averaged using the Nonlinear Mixed Model (PROC NLMIXED) of the SAS software package.

The Generalised Linear Mixed Model (GLMM) approach will be adopted, using STATA statistical software.

The Generalised Linear Mixed Model (GLMM) approach will be used (using STATA statistical software) to take account of the study's cross-over design.