# Faq how RMSEC and RMSECV related to R2Y and Q2Y seen other software

### Issue:

How are RMSEC and RMSECV related to R2Y and Q2Y I see in other software?

### Possible Solutions:

In some software, the values "R2Y" and "Q2Y" are reported for regression models. The R2Y value is equivalent to the y-block cumulative variance captured (as reported in the 5th column of the variance captured table or the .detail.ssq field of a model).

The "Q2Y" value is analogous to R2Y except it is based on the cross-validated results. It is related to the RMSECV values according to this equation :

where RMSECV is the root mean square error of cross-validation, m is the number of samples and yi is the actual (aka measured) y-value for sample #i. These relations are only true if the y-block is mean-centered before the model is built.

R2Y and Q2Y represent fractions of variance captured while the cumulative variance captured table and .detail.ssq field represent percentages. They are identical except for a factor of 100 difference between fraction and percentage.

Given a PLS model named "m" which used only mean centering or autoscaling on the y-block, the following code calculates Q2Y:

incl = m.detail.include{1,2}; y = m.detail.data{2}.data(incl,:); my = length(incl); Q2Y = (1-(m.rmsecv.^2)*my./sum(mncn(y).^2))

The practical aspects of these statistics are:

R2Y and Q2Y generally increase towards 1 as a model's fit improves whereas RMSEC and RMSECV decrease to zero

RMSEC/CV are in units of the original y-block and can be interpreted as "error levels" (They are very similar to standard deviations) whereas R2Y and Q2Y are in fractional units

It is possible for Q2Y to exceed the 0 -> 1 limit if the predicted y-values are particularly bad.)