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When to use Q2 instead of R2 in PLS?
Q2 is the R2 when the PLS built on a training set is applied to a test set. So a good value for Q2 is a value that is close to the R2. That means that your PLS model works independently of the specific data that was used to train the PLS model.
In summary… the Q2 is a statistical measure of the “goodness of prediction” (0-1) 0= no prediction ability in the model 1= perfect prediction of the model.
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What’s the difference between your 2 and q 2?
R 2 is inflationary and rapidly approaches unity as model complexity (number of model parameters) increases. Therefore, it is not sufficient only to have a high R 2. Q 2, on the other hand, is not inflationary and at a certain degree of complexity will not improve any further and then degrade.
Is it possible to calculate Q2 and r2 values?
Since PLS-DA is a computational technique which deals with outcomes expressed as a categorical variable (e.g. “Yellow”,”Brown”,”Black”,”Green”) I cannot understand how it is possible to calculate Q2 and R2 values.
What’s the significance of a PLS / OPLS model?
Concerning the Q2 parameter, a significance threshold of 0.5 is generally admitted. However, during the last few years, many PLS-DA/OPLS-DA models built using SIMCA have been published with Q2 values lower than 0.5.
What is the q 2 of plsda cross validation?
Cross validation revealed a Q 2 value of -0.18 which is usually considered not to be a good classification model. However, the PLSDA score plot in Fig. 1 shows a clear separation between the two classes. PLSDA is eager to please and thus its results should be handled with great care.