NLMENonlinear Mixed Effects (stochastic analysis)
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We may again consider the mechanistic NLME model (2) to address censoring in the covariates.
nlme: Linear and nonlinear mixed effects models (R Package Version 3.1-117).
Data were analysed in R version 3.2.4 (R Core Team, 2016), using the nlme package version 3.1-125 (Pinheiro et al., 2016) for multilevel models.
We employed linear mixed models (LMMs, package nlme, Pinheiro et al.
- First-in-class support for executing Phoenix NLME jobs on remote/parallel computing platforms right from the desktop application,
Results were generated using the software R (R Core Team, 2015) with use of the nlme package (Pinheiro, Bates, Debroy, & Sarkar, 2014) and SAS version 9.4 (Statistical Analysis System [SAS], 2015) with use of the mixed procedure.
The software packages IBM SPSS Statistics for Windows, version 22.0 (IBM Corp.) and R version 3.0.2 (R Foundation for Statistical Computing; packages nlme, ppcor, mgcv, segmented, Hmisc, ROCR, PredictABEL, and ICC) were used for statistical analyses, and the significance level was 0.05 for all models.
In the free open-source stats package R Studio there are two mixed models, lme4 and nlme. Neither offers negative variance, and worse still, they do not provide standard errors for the random effects, so you have no idea of the uncertainty in the standard deviations.