NDLI: Fitting Longitudinal Mixed Effects Logistic Models in S-Plus

Content Provider	IEEE Xplore Digital Library
Author	Lichun Jiang Yaoxiang Li
Copyright Year	2008
Description	Author affiliation: Coll. of Forestry, Northeast Forestry Univ., Harbin, China (Lichun Jiang) \|\| Coll. of Eng. & Technol., Northeast Forestry Univ., Harbin, China (Yaoxiang Li)
Abstract	Statistical models in which both fixed and random effects enter nonlinearly are becoming increasingly popular. These models have a wide variety of applications in many areas such as agriculture, forestry, biology, ecology, biomedicine, sociology, economics, pharmacokinetics, and other areas. Mixed effect models are flexible models to analyze grouped data including longitudinal data, repeated measures data, and multivariate multilevel data. One of the most common applications is nonlinear growth data. In this study, tree growth data set from forestry is used for nonlinear mixed-effects analysis. Nonlinear mixed-effects models involve both fixed effects and random effects. The process of model building for nonlinear mixed-effects models is to determine which parameters should be random effects and which should be purely fixed effects, as well as procedures for determining random effects variance-covariance matrices (e.g. diagonal matrices) to reduce the number of the parameters in the model. Autocorrelation structure was considered for explaining the dependency among repeated measurements within the each individual. Information criterion statistics (AIC, BIC and likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software.
Starting Page	251
Ending Page	254
File Size	254989
Page Count	4
File Format	PDF
ISBN	9781424437467
DOI	10.1109/ISCSCT.2008.112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Biological system modeling Sociology Buildings Forestry mixed effects Agriculture Environmental factors Biomedical measurements Logistic model Logistics Computational biology
Content Type	Text
Resource Type	Article

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