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Partial least squares regression as an alternative to current regression methods used in ecology
Author(s) -
Carrascal Luis M.,
Galván Ismael,
Gordo Oscar
Publication year - 2009
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1111/j.1600-0706.2008.16881.x
Subject(s) - partial least squares regression , statistics , principal component analysis , regression , regression analysis , principal component regression , sample size determination , variance (accounting) , mathematics , sample (material) , variables , regression diagnostic , linear regression , ecology , econometrics , biology , polynomial regression , chemistry , accounting , chromatography , business
This paper briefly presents the aims, requirements and results of partial least squares regression analysis (PLSR), and its potential utility in ecological studies. This statistical technique is particularly well suited to analyzing a large array of related predictor variables (i.e. not truly independent), with a sample size not large enough compared to the number of independent variables, and in cases in which an attempt is made to approach complex phenomena or syndromes that must be defined as a combination of several variables obtained independently. A simulation experiment is carried out to compare this technique with multiple regression (MR) and with a combination of principal component analysis and multiple regression (PCA+MR), varying the number of predictor variables and sample sizes. PLSR models explained a similar amount of variance to those results obtained by MR and PCA+MR. However, PLSR was more reliable than other techniques when identifying relevant variables and their magnitudes of influence, especially in cases of small sample size and low tolerance. Finally, we present one example of PLSR to illustrate its application and interpretation in ecology.