From R-squared to coefficient of model accuracy for assessing "goodness-of-fits"

dc.contributor.authorOnyutha, Charles
dc.date.accessioned2022-09-14T20:04:41Z
dc.date.available2022-09-14T20:04:41Z
dc.date.issued2020
dc.description.abstractModelers tend to focus more on advancing methods of statistical and mathematical modeling than developing novel techniques for comparing modeled results with observations or establishing metrics for model performance assessment. Perhaps solely the most extensively applied "goodness-of-fit" measure especially for assessing performance of regression models is the coefficient of determination R2. Normally, high R2 tends to be associated with an efficient model. Nevertheless, R2 has been cited to have no importance in the classical model of regression. Even in its use in descriptive statistics, R2 is known to have questionable justification. R2 is inadequate in assessing model performance because it does not give any information on the model residuals. Furthermore, R2 can be low for an effective model. Contrastingly, a very poor model fit can yield high R2. Regressing X on Y yields R2 which is the same as that if Y is regressed on X thereby invalidating its use as a coefficient of determination. Taking into account the drawbacks of using R2, this paper introduces coefficient of model accuracy (CMA) the derivation of which comprises an analogy to the R2. However, instead of simply squaring an ordinary Pearson's product-moment correlation coefficient to obtain R2, CMA comprises the product of nonparametric sample correlation and model bias. Acceptability of the introduced method can be found demonstrated through comparison of results from simulations by hydrological models calibrated using CMA and other existing objective functions. MATLAB and R codes as well as an illustrative MS Excel file to compute the CMA were provided.en_US
dc.identifier.citationOnyutha, C. (2020). From R-squared to coefficient of model accuracy for assessing "goodness-of-fits". Geoscientific Model Development Discussions , 1-25. Muñozhttps:// doi.org/10.5194/gmd-2020-51en_US
dc.identifier.urihttps:// doi.org/10.5194/gmd-2020-51
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4742
dc.language.isoenen_US
dc.publisherGeoscientific Model Development Discussionsen_US
dc.titleFrom R-squared to coefficient of model accuracy for assessing "goodness-of-fits"en_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
From R-squared to coefficient of model accuracy for assessing.pdf
Size:
1.23 MB
Format:
Adobe Portable Document Format
Description:
Article
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: