Multivariate Generalized Linear Mixed Models for Ranking Sports Teams

Description

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.

Details

See the help pages for mvglmmRank and game.pred

Author(s)

Andrew T. Karl and Jennifer Broatch

Maintainer: Andrew T. Karl <akarl@asu.edu>

References

Karl, A.T., Yang, Y. and Lohr, S. (2013). Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments. Computational Statistics and Data Analysis, 59, 13-27.

Karl, A., Yang, Y. and Lohr, S. (2014) Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects Computational Statistics & Data Analysis 73, 146–162.

Karl, A.T. (2012). The Sensitivity of College Football Rankings to Several Modeling Choices, Journal of Quantitative Analysis in Sports, Volume 8, Issue 3, DOI 10.1515/1559-0410.1471

Examples

data(nfl2012)
mvglmmRank(nfl2012,method="PB0",first.order=TRUE,verbose=TRUE,max.iter.EM=1)

result <- mvglmmRank(nfl2012,method="PB0",first.order=TRUE,verbose=TRUE)
print(result)
game.pred(result,home="Denver Broncos",away="Green Bay Packers")

Results