Sentence examples for were root mean square from inspiring English sources

GF indices were: Root mean square error of approximation (0.031), GF index (0.97), and normalized fit index (0.92).

Parameters used to evaluate the models prediction ability were: root mean square error prediction (RMSEP) and coefficient of determination (r) for the derived model between actual and predicted Y-variables (Capone et al., 2013).

The model fit criteria were root mean square error of approximation (RMSEA; preferably less than 0.08), comparative fit index (CFI; preferably at least 0.95) and Tucker-Lewis index (TLI; preferably at least 0.95).

where RMSE is Root Mean Square Error, and it is defined as follows: (18).

The goodness of fit test of the originally hypothesized structure resulted in Satorra-Bentler chi-square (df 167) = 1617, p <.00001 and the goodness of fit statistics were: root mean-square error of approximation RMSEA = 0.12 (95% Confidence Interval, CI: 0.11 – 0.13); comparative fit index CFI = 0.87; normed fit index NFI = 0.85 and the non-normed fit index NNFI = 0.85.

The evaluation metrics used are Root Mean Squared Error (RMSE), Area Under the Curve (AUC), Area Under the Precision-Recall curve (AUPR) and Concordance Index (CI).

To determine the performance of the investigated methods and also to compare the models with each other, the statistical parameters used are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) and average absolute deviation.

The EMG recordings were root-mean-square (RMS) converted in 0.1-s periods, adjusted for noise, normalized to submaximal reference contractions, and expressed in % RVE (reference voluntary electrical activity) (Mathiassen et al. 1995).

There were root-mean-square deviations (rmsds) from the experimental distances and dihedral constraints, from the energetic statistics (FNOE, Ftor, Frepel, and EL-J), and from the idealized geometry.

Average roughness (Ra)—the arithmetic average of a deviationy, from the center line is: Root-mean-square roughness (Rrms) is the root-mean-square deviation from center line: For each sample, the rms roughness and average roughness as defined in [21] were evaluated.

The similarity measure to group the MD sampled conformations is root-mean-square deviation (RMSD) in this study.