Criterios de información en la selección multimodelo de regresiones paramétricas: aplicaciones biológicas (2015)
In carrying out modelling analysis of experimental data results important to obtain a measure of the relative fit of the models as a primary selection criterion. In this sense, there are few studies based on multi-model selection techniques for the theoretical representation of data sets, so it is common to incur in a misinterpretation of the existing patterns, or even more, the incorrect extrapolation and prediction based on the wrong model. This paper is intended to evaluate in 40 sets of data from various publications researches the effectiveness of the regression model designated by the authors by contrasting six regression models with the Akaike and Bayesian information criteria and to discuss its implications on subsequent interpretations made. It was found that the linear regression model was successful only in 13.35% of the datasets (AIC= 15%; BIC = 11.7%), but in the other hand, the logarithmic model was the most successful model in 38.5% of the cases (AIC= 35%; BIC= 41.1%) which casts doubt on the efficiency of the linear regression over other types of regression under biological data. It is clear then that the features discussed from regression analysis regardless multi-model selection depends on the subjectivity of the researcher and often incurs in selecting a model that involves greater losses of the information contained in the data set.