Date of Award

December 2020

Document Type


Degree Name

Master of Science (MS)


School of Mathematical and Statistical Sciences

Committee Member

Patrick Gerard

Committee Member

Deborah Kunkel


Linear quantile mixed modeling is a diverse statistical tool that can replace traditional least squares modeling for analyzing data whose sampling method has some form of clustering and whose response has trends that differ for each quantile level. In this article, we will evaluate the effectiveness of this modeling method through the use of the lqmm package in R[2]. Simulations and n applied data analysis will be performed to evaluate the performance of the lqmm() function on different types of datasets. We will also introduce background on quantile based mixed modeling and give descriptions of the output and main commands of the lqmm() function.



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