the residual error of each data point (i.e., individual response) is based on a model trained on all the data except for that point.
the predictive R 2 is the predicted residual error sum of squares (PRESS) statistic (Allen, 1974) divided by the total sum of squares (SS total ): That is, for each individual response i ∈ D, we sum the residual squared error between the true value, logRespDev i and the value predicted by a model trained on the rest of the data, logRespDev i,D\i .