TY - JOUR
T1 - On correlation analysis of many-to-many observations
T2 - an alternative to Pearson's correlation coefficient and its application to an ecotoxicological study
AU - Moltchanova, Elena
AU - Gerhard, Daniel
AU - Mohamed, Fathimath
AU - Gaw, Sally
AU - Glover, Chris N.
N1 - Publisher Copyright:
© 2017 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd.
PY - 2017/12
Y1 - 2017/12
N2 - Correlation studies are an important hypothesis-generating and testing tool, and have a wide range of applications in many scientific fields. In ecological studies in particular, multiple environmental variables are often measured in an attempt to determine relationships between chemical, physical and biological factors. For example, one may wish to know whether and how soil properties correlate with plant physiology. Although correlation coefficients are widely used, their properties and limitations are often imperfectly understood. This is especially the case when one is interested in correlations between, say, trace element content in sediments and in marine organisms, where no one-to-one correspondence exists. We show that evaluating Pearson's correlation coefficient for either site-specific means or composite samples results in biased estimates, and we propose an alternative estimator. We use simulation studies to demonstrate that our estimator generally has a much smaller bias and mean squared error. We further illustrate its use in a case study of the correlation between trace element content in sediments and in mussels in Lyttelton Harbour, New Zealand.
AB - Correlation studies are an important hypothesis-generating and testing tool, and have a wide range of applications in many scientific fields. In ecological studies in particular, multiple environmental variables are often measured in an attempt to determine relationships between chemical, physical and biological factors. For example, one may wish to know whether and how soil properties correlate with plant physiology. Although correlation coefficients are widely used, their properties and limitations are often imperfectly understood. This is especially the case when one is interested in correlations between, say, trace element content in sediments and in marine organisms, where no one-to-one correspondence exists. We show that evaluating Pearson's correlation coefficient for either site-specific means or composite samples results in biased estimates, and we propose an alternative estimator. We use simulation studies to demonstrate that our estimator generally has a much smaller bias and mean squared error. We further illustrate its use in a case study of the correlation between trace element content in sediments and in mussels in Lyttelton Harbour, New Zealand.
KW - Pearson's correlation coefficient
KW - analysis of variance and covariance
KW - ecological science
KW - mixed models
KW - mussels
KW - sediment
KW - toxicity
KW - trace elements
UR - http://www.scopus.com/inward/record.url?scp=85035037977&partnerID=8YFLogxK
U2 - 10.1111/anzs.12211
DO - 10.1111/anzs.12211
M3 - Journal Article
AN - SCOPUS:85035037977
SN - 1369-1473
VL - 59
SP - 371
EP - 387
JO - Australian and New Zealand Journal of Statistics
JF - Australian and New Zealand Journal of Statistics
IS - 4
ER -