<p>A: Variables importance for the projection (VIP) of Carrying hose baskets up stairs work capacity, using the training-set (n = 36). Green bars: Variables included in final the model. Blue bars: Variables were excluded from the model in Step 1, using VIP. Red bars: Variables were excluded from the model in Step 2 because the correlation was r ≥ 0.8 with another included variable. B: External validation is testing the selected model on the prediction-set.</p
<p>Gray bars indicate how well the model performs with only that variable, versus a full model. Valu...
Importance of variables as measured by partial Wald χ2 minus the predictor degrees of freedom in the...
<p>Training gain for each predictor variable alone (black) and the loss in training gain when the va...
<p>A: Variables importance for the projection (VIP) of Carrying hose baskets over terrain work capac...
<p>A: Variables importance for the projection (VIP) of Hose pulling work capacity, using the trainin...
<p>A: Variables importance for the projection (VIP) of Demolition work capacity, using the training-...
<p>Selection of valid physical tests for prediction of Carrying hose baskets over terrain (n = 38) u...
<p>Selection of valid physical tests for prediction of firefighting physical work capacity (n = 36–3...
<p>Bivariate correlations (Pearson r) for simulated work tasks performance time (s): Carrying hose b...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
<p><sup>a</sup> Predictors in the Model: (Constant), MOCA.</p><p><sup>b</sup> Predictors in the Mode...
<p>A VIP score is a measure of a variable’s importance in the PLS-DA model. It summarizes the contr...
<p>Variables included in the final model for occupancy probability across the residential habitat ty...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
<p>Gray bars indicate how well the model performs with only that variable, versus a full model. Valu...
Importance of variables as measured by partial Wald χ2 minus the predictor degrees of freedom in the...
<p>Training gain for each predictor variable alone (black) and the loss in training gain when the va...
<p>A: Variables importance for the projection (VIP) of Carrying hose baskets over terrain work capac...
<p>A: Variables importance for the projection (VIP) of Hose pulling work capacity, using the trainin...
<p>A: Variables importance for the projection (VIP) of Demolition work capacity, using the training-...
<p>Selection of valid physical tests for prediction of Carrying hose baskets over terrain (n = 38) u...
<p>Selection of valid physical tests for prediction of firefighting physical work capacity (n = 36–3...
<p>Bivariate correlations (Pearson r) for simulated work tasks performance time (s): Carrying hose b...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
<p><sup>a</sup> Predictors in the Model: (Constant), MOCA.</p><p><sup>b</sup> Predictors in the Mode...
<p>A VIP score is a measure of a variable’s importance in the PLS-DA model. It summarizes the contr...
<p>Variables included in the final model for occupancy probability across the residential habitat ty...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
<p>Gray bars indicate how well the model performs with only that variable, versus a full model. Valu...
Importance of variables as measured by partial Wald χ2 minus the predictor degrees of freedom in the...
<p>Training gain for each predictor variable alone (black) and the loss in training gain when the va...