Generalized Linear Mixed-Effects Models in R

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$500,000.00
Award Year:
2003
Program:
STTR
Phase:
Phase II
Contract:
DAMD1702C0119
Award Id:
63210
Agency Tracking Number:
A2-0841
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Suite A, 75 Aero Camino, Goleta, CA, 93117
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
054672662
Principal Investigator:
Ben Juricek
Analyst
(805) 968-6787
bjuricek@toyon.com
Business Contact:
Marcy Lindbery
Director of Contracts
(805) 968-6787
mlindbery@toyon.com
Research Institution:
UNIV. OF WISCONSIN-MADISON
Doug Bates
Research & Sponsored Programs, 750 University Ave.
Madison, WI, 53706
(608) 263-8531
Nonprofit college or university
Abstract
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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