Sentence examples for multiple predictor models from inspiring English sources

We fitted multiple predictor models using automated model selection via information theoretic approaches and multi-model averaging using maximum likelihood.

Because the correlation was strongest between MWD and each canopy structure variable (Fig. 4), we excluded AMI, CovP and MAP from subsequent multiple predictor models.

Similarly, the correlation was stronger between MinT and LAI and between MinT and FCover compared to elevation, and we excluded elevation from multiple predictor models for both canopy structure variables.

Multiple predictor models predicting canopy attributes from climatic and disturbance predictors suggested that whilst climate, and in particular MWD, was the main driver of variability in canopy structure across plots, climate interacted with the protection status of a forest in determining forest canopy structure.

In multiple predictor models, we inspected for evidence of multicollinearity.

For all analyses with multiple predictor variables, only those variables that were significant at the p < 0.2 level in single predictor models were included in the multiple predictor models.

We did not construct a multiple predictor model with these data owing to the small sample size.

The probabilities in the multiple predictor model take into account the presence of the other predictors, that is, tumor size, nodal status, grade, PR expression, P7-score and p-mTOR.

To quantify whether predictors identified as important in above multiple-predictor models improved the conditional R2 of MWD based models (see Fig. 4 for details), we directly added MinT to the models predicting LAI and FCover from MWD and we added Ele_Min and Slope to the MWD based model predicting fAPAR.

This is because there are fewer degrees of freedom for the error when testing individual predictors in a multiple-predictor model rather than individually.

22 26 We then examined the associations of all statistically significant (at a 0.05 probability level) destination and other environmental correlates derived from the models described above to decide whether they could be entered in a multiple-predictor model to examine the independent contributions of the identified correlates to non-transport sitting and motorised transport.