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Clinical judgement governed the decision of which predictors to screen for inclusion, but was inevitably tainted by data availability.
In order to determine which predictors in the considered models are statistically significant a stepwise regression analysis with backward elimination was performed.
Random Forest Machine Learning also provides quantitative information on which predictors (e.g., satellite data) are most important in determining the response variable (LiDAR-derived canopy height) [47].
Hierarchical entry was used to enter five predictors into the model based on which predictors had the largest effect in the correlation analysis.
Model comparison was made using Akaike's Information Criterion AICC) to evaluate which predictors were more closely associated with the outcome [33, 34].
Equity on the other hand, can be measured by exploring which predictors of use are dominant, which advances UGS access assessment by shifting from the spatial-based to the use-based.
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Multiple imputation thus requires the building of an imputation model in which predictor variables have to be specified.
However, the compatibility premise requires the false reading in which Predictor and Avoider can finish against each other.
However, the severity of these reductions was highly dependent on which predictor was the most important limiting factor.
To estimate the weight of evidence of each predictor (wpi) to explain the frequency of extinction and colonisation, we simply summed the model AICs weights (wi) over all models in which predictor appeared.
The order in which predictor variables enter the model was used to rank their relative importance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com