Journal
ECOLOGICAL MODELLING
Volume 216, Issue 3-4, Pages 316-322Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecolmodel.2008.05.006
Keywords
measured values; simulated values; regression; slope; intercept; linear models; regression coefficient; goodness-of-fit; 1 : 1 line
Categories
Funding
- University of Buenos Aires
- Proyecto Estrategico Res. (CS) [5988/01]
- IAI-CRN [112031]
- FONCYT [PICT 06-1764]
- UBACYT [G-071, G-078]
- CONICET, Argentina
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A common and simple approach to evaluate models is to regress predicted vs. observed values (or vice versa) and compare slope and intercept parameters against the 1:1 line. However, based on a review of the literature it seems to be no consensus on which variable (predicted or observed) should be placed in each axis. Although some researchers think that it is identical, probably because r(2) is the same for both regressions, the intercept and the slope of each regression differ and, in turn, may change the result of the model evaluation. We present mathematical evidence showing that the regression of predicted (in the y-axis) vs. observed data (in the x-axis) (PO) to evaluate models is incorrect and should lead to an erroneous estimate of the slope and intercept. In other words, a spurious effect is added to the regression parameters when regressing PO values and comparing them against the 1:1 line. observed (in the y-axis) vs, predicted (in the x-axis) (OP) regressions should be used instead. We also show in an example from the literature that both approaches produce significantly different results that may change the conclusions of the model evaluation. (C) 2008 Elsevier B.V. All rights reserved.
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