Dear all,
I am a statistician doing research in QSAR, building regression models where
the dependent variable is a numerical expression of some chemical activity and
input variables are chemical descriptors, e.g. molecular weight, number of
carbon atoms, etc.
I am building regression models and I am confronted with a widely a technique
called Y-RANDOMIZATION for which I have difficulties in finding references in
general statistical literature regarding regression analysis. I would be
grateful if someone could point me to papers/literature in statistical
regression analysis which give scientific (statistical) foundation for using
Y-RANDOMIZATION.
Y-RANDOMIZATION is a widely used technique in QSAR community to unsure the
robustness of a QSPR (regression) model. It is used after the "best" regression
model is selected and to make sure that there are no chance correlations. Here
is a short description. The dependent variable vector (Y-vector) is randomly
shuffled and a new QSPR (regression) model is fitted using the original
independent variable matrix. By repeating this a number of times, say 100
times, one will get hundred R2 and q2 (leave one out cross-validation R2) based
on hundred shuffled Y. It is expected that the resulting regression models
should generally have low R2 and low q2 values. However, if the majority of
hundred regression models obtained in the Y-randomization have relatively high
R2 and high q2 then it implies that an acceptable regression model cannot be
obtained for the given data set by the current modelling method.
I cannot find any references to Y-randomization or Y-scrambling anywhere in the
literature outside chemometrics/QSAR. Any links or references would be much
appreciated.
Thanks in advance.
DK
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Damjan Krstajic
Director
Research Centre for Cheminformatics
Belgrade, Serbia
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