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Before selecting pixels to match with ground plots for all predictor datasets, we masked out major rivers and paved roads by on-screen digitizing using Landsat data at 1 10,000.
We used spatial analyst in ArcGIS to develop all raster images and to extract cell values from raster images to location data (i.e., used and non-used points) for all predictor variables.
Our analyses indicate that there is no autocorrelation in the sample (Durbin-Watson value of 2.124) and no collinearity problem (VIF values for all predictor variables are between 1.034 and 1.316).
As (W^_{ text {train})} alpha V)=W^_{ text {train})}(V)) for all predictor weights V and positive constants α>0, one may assume without loss of generality that predictor weights are always scaled such that their components sum to unity, see, e.g., (Abadie and Gardeazabal 2003, p. 128), (Abadie et al. 2015, Footnote 5, p. 497).
Descriptive statistics will be derived for all predictor and outcome variables.
Statistically significant outputs for all binomial regression models for all predictor variables are presented below.
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Our models estimated unadjusted and adjusted hazard ratios (HRs) (controlling for all other predictors) and 95% confidence intervals (CIs) for all the predictors of interest.
Bivariate correlations were calculated for all predictors (see Table 6).
The Variance Inflation Factor (2.539 5.573) and Tolerance (.179 .394) indices for all predictors do not show any critical values.
Importance scores were estimated for all predictors that were included in the final non-parametric models of the 300 Index.
These chemical shift errors were partitioned on 0.1 ppm intervals up to 0.35 ppm, a value that already comprises over 90% of the predictions for the best performing method and over 80% for all predictors evaluated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com