The projections for the two available chains, B and C (chain A had unresolved residues within this region), were both found to be 2.8-fold greater than the mean square fluctuation seen in the WT trajectory along this eigenvector.
From Figure 8, when w = 0.9, its mean values of Accuracy, Precision, and F1 are the greatest, and the mean square error is the smallest.
The grouped analysis revealed one statistically significant comparison between barefoot trials for the root mean square greater than 60 Hz variable.
Note that sFIT is greater than the root mean square error (RMSE); this is because the means and X0 and the calculated coefficient vector b are themselves estimates that are subject to sampling error, with 1 and p degrees of freedom, respectively.
For the present investigation, we utilized multiple indices of model fit: a chi-square to degrees of freedom ratio (χ2/df) less than three, comparative fit index (CFI) values greater than 0.95, root mean square error of approximation (RMSEA) values less than 0.06, and weighted root-mean-square residual (WRMR) values less than 0.90 [24], [24].
Criteria to evaluate the good fit of the path model to the observed data are Chi-Square Test of Model Fit with p values greater than 0.05, Root Mean Square Error Of Approximation (RMSEA) with values less than 0.05, and Tucker-Lewis Index (TLI) and Comparative Fit Index (CFI) with values greater than 0.90 [ 54].
These models would be validated if the comparative fit index (CFI) and the incremental fit index (IFI) were equal to or greater than.90, the root mean square error of approximation (RMSEA) was less than or equal to.05, and 90% confidence interval (CI) of RMSEA ranged from 0 to 0.08 [ 22].
Standardised root mean squared residuals (SRMR) of less than 0.08, comparative fit index (CFI) greater than 0.95, Tucker Lewis index (TLI) greater than 0.95 and root mean square error approximation (RMSEA) less than 0.06, were used for assessing fitness of the CFA model.
It shows that the between-sample mean square is greater than the within-sample mean square, and the F test shows that this difference is highly significant.
P values greater than 0.05 and an adjusted goodness-of-fit (AGFI) greater than 0.90 with a root mean square error of approximation (RMSEA) of 0.05 (0.08) or less indicated that the model adequately described the observed data.
