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Generalized Linear Mixed-Effects Models in R

Award Information
Agency: Department of Defense
Branch: Army
Contract: DAMD1702C0119
Agency Tracking Number: A2-0841
Amount: $0.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2003
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
Suite A, 75 Aero Camino
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ben Juricek
 (805) 968-6787
Business Contact
 Marcy Lindbery
Title: Director of Contracts
Phone: (805) 968-6787
Research Institution

The University of Wisconsin and Toyon Research Corporation propose developing a software package in the R statistical environment for estimating generalized linear mixed-effects models (GLMMs) from multilevel, categorical data (e.g., binary, ordinal ormultinomial data). The proposed software package should enable data analysts and researchers to more accurately model and understand this type of survey data. The package is aimed toward a user who may be familiar with mixed-effects models, but is not anexpert in the numerical procedures required to estimate GLMMs.Estimating the parameters for a GLMM can be a computationally intensive task, and remains an active field of academic and industrial research. Furthermore, a brute force approach, which may converge extremely slowly on a desktop computer, is unlikely toestimate an accurate GLMM. In our proposed approach, we avoid a large computational problem from the outset by proceeding in a staged fashion. We begin with an approximate solution, and gradually increase the model accuracy using more numerically intensivemethods. In this staged approach, the most numerically intensive methods operate on a smaller, better defined problem, which results in a fast and accurate solution. The successful completion of this research will result in extensions to the NLME R packagethat will enable statisticians and analysts to build generalized linear mixed-effects models for binary and ordinal data, emphasizing data collected in longitudinal studies.

* Information listed above is at the time of submission. *

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