Backward procedure for joint selection of covariates and random effects
Backward procedure for joint selection of covariates and random effects
Joint selection of covariates and random effects in a nonlinear mixed effects model by a backward-type algorithm based on two different versions of BIC for covariate selection and random effects selection respectively. Selection is made among the covariates as such specified in the SaemixData object. Only uncorrelated random effects structures are considered.
backward.procedure(saemixObject, trace =TRUE)
Arguments
saemixObject: An object returned by the saemix function
trace: If TRUE, a table summarizing the steps of the algorithm is printed. Default "TRUE"
Returns
An object of the SaemixObject class storing the covariate model and the covariance structure of random effects of the final model.
References
M Delattre, M Lavielle, MA Poursat (2014) A note on BIC in mixed effects models. Electronic Journal of Statistics 8(1) p. 456-475 M Delattre, MA Poursat (2017) BIC strategies for model choice in a population approach. (arXiv:1612.02405)