forward.procedure: Backward procedure for joint selection of covariates and...
In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm
forward.procedure R Documentation
Backward procedure for joint selection of covariates and random effects
Description
Joint selection of covariates and random effects in a nonlinear mixed effects model by a forward-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.
Usage
forward.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"
Value
An object of the SaemixObject class storing the covariate model and the covariance structure of
random effects of the final model.
Author(s)
Maud Delattre
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)
saemix documentation built on May 29, 2024, 10:43 a.m.
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| forward.procedure | R Documentation |
Backward procedure for joint selection of covariates and random effects
Description
Joint selection of covariates and random effects in a nonlinear mixed effects model by a forward-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.
Usage
forward.procedure(saemixObject, trace = TRUE)
Arguments
saemixObject |
An object returned by the |
trace |
If TRUE, a table summarizing the steps of the algorithm is printed. Default "TRUE" |
Value
An object of the SaemixObject class storing the covariate model and the covariance structure of random effects of the final model.
Author(s)
Maud Delattre
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)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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