Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation maximisation (SAEM) algorithm. In the present paper we describe the main features of the package,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2017-08-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/2399 |
| _version_ | 1818243361476182016 |
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| author | Emmanuelle Comets Audrey Lavenu Marc Lavielle |
| author_facet | Emmanuelle Comets Audrey Lavenu Marc Lavielle |
| author_sort | Emmanuelle Comets |
| collection | DOAJ |
| description | The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community. |
| first_indexed | 2024-12-12T13:59:54Z |
| format | Article |
| id | doaj.art-7dd46b7f783c40b3a6bf433a511efdce |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-12T13:59:54Z |
| publishDate | 2017-08-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-7dd46b7f783c40b3a6bf433a511efdce2022-12-22T00:22:21ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602017-08-0180114110.18637/jss.v080.i031139Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM AlgorithmEmmanuelle CometsAudrey LavenuMarc LavielleThe saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community.https://www.jstatsoft.org/index.php/jss/article/view/2399nonlinear mixed effect modelsstochastic approximation EM algorithmpharmacokineticspharmacodynamicstheophyllineorange treeS4 classes |
| spellingShingle | Emmanuelle Comets Audrey Lavenu Marc Lavielle Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm Journal of Statistical Software nonlinear mixed effect models stochastic approximation EM algorithm pharmacokinetics pharmacodynamics theophylline orange tree S4 classes |
| title | Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm |
| title_full | Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm |
| title_fullStr | Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm |
| title_full_unstemmed | Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm |
| title_short | Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm |
| title_sort | parameter estimation in nonlinear mixed effect models using saemix an r implementation of the saem algorithm |
| topic | nonlinear mixed effect models stochastic approximation EM algorithm pharmacokinetics pharmacodynamics theophylline orange tree S4 classes |
| url | https://www.jstatsoft.org/index.php/jss/article/view/2399 |
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