The k-fold crossvalidation method randomly divides all of the (non-excluded) rows in the datatable, D, into k subsets: D1, D2, . . . , Dk. Each row is randomly assigned to one of the k groups.
First, a model is fit using all of the data. Then, K different models are fit, using the same splits, with data from D - Di, where Di is the holdout fold. For continuous responses, the error for each observation in Di is calculated using the model, evaluated from D - Di. For nominal and ordinal responses, JMP® calculates -2LogLikelihood for each observation in Di using the model, trained from D - Di. This is repeated for each of the k folds. The root mean square errors for each of the k folds are squared and summed (or in the case of nominal and ordinal responses, the -2loglikelihood values are summed) to construct the crossvalidation SSE (or crossvalidation -2loglikelihood in the case of nominal and ordinal responses). The resulting folded SSE is then used to calculate a folded R square value.
[Previously JMP Note 36388]