Date of Award
December 2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
School of Mathematical and Statistical Sciences
Committee Member
Patrick Gerard
Committee Member
Deborah Kunkel
Abstract
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.
Recommended Citation
DeMarco, Thomas Charles, "Linear Quantile Mixed Modeling: A Study of the 'lqmm' Package in R" (2020). All Theses. 3447.
https://tigerprints.clemson.edu/all_theses/3447