Determining the degree of randomness of descriptors in linear regression equations with respect to the data size.
Linear regression equations suffer from the curse of dimensionality that leads to overfitting and accidental correlation, particularly for small data sets and when many variables are present. This can lead to cases where descriptors based on random numbers exhibit higher correlations than actual descriptors. In this study, it was therefore investigated how high the degree of accidental correlation of a single descriptor can be with respect to the number of observations. On the basis of computer simulations for data sizes ranging from 7 to 500 observations, a formula was derived that expresses the degree of randomness (in percent) of a chosen descriptor depending on its correlation coefficient and the size of the data set. This allows one to determine a cutoff for the correlation below which descriptors can be discarded due to a high risk of chance correlation. Doing so, the number of eligible variables for the regression analysis can be reduced substantially. Corresponding applications are reported for several QSAR data sets of various sizes.
Center for Bioinformatics, Campus Building E2.1, Saarland University, 66123 Saarbrücken, Germany. michael.hutter@bioinformatik.uni-saarland.de
Michael C
MC
Hutter
D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D016014:Linear Models; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship