Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Response variable selection in principal response curves using permutation testing

Response variable selection in principal response curves using permutation testing Principal response curves analysis (PRC) is widely applied to experimental multivariate longitudinal data for the study of time-dependent treatment effects on the multiple outcomes or response variables (RVs). Often, not all of the RVs included in such a study are affected by the treatment and RV-selection can be used to identify those RVs and so give a better estimate of the principal response. We propose four backward selection approaches, based on permutation testing, that differ in whether coefficient size is used or not in ranking the RVs. These methods are expected to give a more robust result than the use of a straightforward cut-off value for coefficient size. Performance of all methods is demonstrated in a simulation study using realistic data. The permutation testing approach that uses information on coefficient size of RVs speeds up the algorithm without affecting its performance. This most successful permutation testing approach removes roughly 95 % of the RVs that are unaffected by the treatment irrespective of the characteristics of the data set and, in the simulations, correctly identifies up to 97 % of RVs affected by the treatment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aquatic Ecology Springer Journals

Response variable selection in principal response curves using permutation testing

Aquatic Ecology , Volume 51 (1) – Oct 15, 2016

Loading next page...
 
/lp/springer-journals/response-variable-selection-in-principal-response-curves-using-wQ8FcYAI80

References (23)

Publisher
Springer Journals
Copyright
Copyright © 2016 by The Author(s)
Subject
Life Sciences; Freshwater & Marine Ecology; Ecosystems
ISSN
1386-2588
eISSN
1573-5125
DOI
10.1007/s10452-016-9604-1
Publisher site
See Article on Publisher Site

Abstract

Principal response curves analysis (PRC) is widely applied to experimental multivariate longitudinal data for the study of time-dependent treatment effects on the multiple outcomes or response variables (RVs). Often, not all of the RVs included in such a study are affected by the treatment and RV-selection can be used to identify those RVs and so give a better estimate of the principal response. We propose four backward selection approaches, based on permutation testing, that differ in whether coefficient size is used or not in ranking the RVs. These methods are expected to give a more robust result than the use of a straightforward cut-off value for coefficient size. Performance of all methods is demonstrated in a simulation study using realistic data. The permutation testing approach that uses information on coefficient size of RVs speeds up the algorithm without affecting its performance. This most successful permutation testing approach removes roughly 95 % of the RVs that are unaffected by the treatment irrespective of the characteristics of the data set and, in the simulations, correctly identifies up to 97 % of RVs affected by the treatment.

Journal

Aquatic EcologySpringer Journals

Published: Oct 15, 2016

There are no references for this article.