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I used k folds cross validation to calculate the predective error of my linear mixed-effects model. I computed it manually because the caret package is not available from lmer model of lme4 package. My question is: how can summarize k folds cross validation results using someindex and some plots to understand if my model make good prediction? I calculated MSE for each folds and the following index:

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Thanks so much

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You can just average over the results of the k-folds. Do note that k-fold cross validation is used to see which model works best, not to judge the expected error of the final model on new data. To do the final testing, use data that you (hopefully) kept separate during the entire modelling phase.

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  • $\begingroup$ Thanks @dimpol for the response. Precisely I segmented my data into 10 folds. I used iteratively the train data (all dataset without a k fold) to fit the model, and the test data (the dataset only in the k fold) to calculate prediction. Finally I calculate MSE for each iteration( k iteration). is it incorrect? $\endgroup$
    – Angela Andreella
    Commented Oct 20, 2016 at 10:46

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