Variance Components Testing for Linear and Nonlinear Mixed Effects Models
alt.desc
Monte Carlo approximation of chi-bar-square weights
Compute the inverse of the Fisher Information Matrix using parametric ...
Compute the inverse of the Fisher Information Matrix using parametric ...
Approximation of the inverse of the Fisher Information Matrix via para...
Compute the inverse of the Fisher Information Matrix using parametric ...
Chi-bar-square degrees of freedom computation
Extract FIM
Extract model structure
Extract model structure
Extracting models' structures
Extract model structure
Extract covariance matrix
Extract covariance matrix
Extract covariance matrix
Extract the Fisher Information Matrix
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Internal functions for constrained minimization
Extract package name from a fitted mixed-effects model
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print.res.message
Summary
Variance component testing
Monte Carlo approximation of chi-bar-square weights
An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.