Fig. 7.

Assessing model fit for between-subject pain. Histograms, kernel density estimates, and average bootstrap estimates for R-squared, adjusted R-squared, Root-mean-square Error (RMSE) and model significance afforded by F-test (determines if a model fits significantly better than one based on a constant term only). Means and percentile intervals for the first three figures: $\overline{R^2}=$0.286, CI_95% = [0.079, 0.475]; $\overline{R_{\mathit{adj}.}^2}$= 0.207, CI_95% = [−0.023, 0.417]; $\overline{\mathit{\text{RMSE}}}$ = 37.238, CI_95% = [31.247, 43.404]. For model significance: 65.9% of models were significant at the .05 level, p_median = .023