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Model fitting was undertaken using the generalised linear latent and mixed models software implemented in Stata (version 10, StataCorp, College Station, TX, USA).
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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 fit two general linear mixed models.
Using a Mixed Model Analysis under a Bayesian approach by the Gene Expression Analysis with Mixed Models (GEAMM) software [ 35], the relationship between neuropathological lesions and gene expression was analyzed.
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 statistical analyses were Pearson correlations and Mixed Models analyses in SAS® software.
These analyses were performed under R software using 'ape' [ 22] and 'MCMCglmm' (Hadfield, J: MCMC methods for Multi-response Generalised Linear Mixed Models: The MCMCglmm R package, submitted) packages.
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 computational demands of fitting such a model are an issue with the asreml software used in the MVMPWAIM package or indeed with any mixed-models software that might be used.
The population pharmacokinetic model was developed using the non-linear mixed-effects modelling software nonmem (version 7.2; Icon Development Solutions, Ellicott City, MD, USA).
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