Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

ABSTRACT

The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates. We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.

Keywords:

• Compositional data
• centered logratio coordinates
• rounded zeros
• partial least squares regression
• cluster analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Karel Hron  http://orcid.org/0000-0002-1847-6598

Additional information

Funding

The authors wish to thank the support provided by Natural Science Foundation of Shanxi Province of China [grant number 2015011044], Shanxi International Science and Technology Cooperation Project [grant number 2015081020], Graduate Education Innovation Project of Shanxi Province [grant number 2017BY001] and by the grant COST Action CRoNoS IC1408.