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Data reproducibility is a critical issue in all scientific experiments. Inthis manuscript, we consider the problem of quantifying the reproducibility ofgraphical measurements. We generalize the concept of image intra-classcorrelation coefficient (I2C2) and propose the concept of the graphicalintra-class correlation coefficient (GICC) for such purpose. The concept ofGICC is based on multivariate probit-linear mixed effect models. We willpresent a Markov Chain EM (MCEM) algorithm for estimating the GICC. Simulationsresults with varied settings are demonstrated and our method is applied to theKIRBY21 test-retest dataset.

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models