Sentence examples for classes of predictors from inspiring English sources

Our results demonstrated that the full models, which integrated all three classes of predictors, were superior to other models with significantly higher AUC values (p<0.001), and was consistent for all three turtle species.

For each of the three broadly distributed turtles, namely, Macrochelodina expansa, Chelodina longicollis and Emydura macquarii, we built five sets of Maxent (maximum entropy modelling) SDMs, using a combination of the three classes of predictors (climatic, topographic, and remotely sensed).

Somewhat surprisingly, climatic EGVs were comparatively less influential than other classes of predictors.

The specific classes of predictors being measured are socio-demographic characteristics, general health, previous history, and psychological characteristics (see additional file 1).

Finally, we report the adjusted R for models with each of four classes of predictors (i.e. plant species, soil chemistry, spatial location and plant phylogeny) individually, to estimate the maximum relative explanatory power of each, adjusted for the number of predictors in the model.

Our modeling approach is based on the assumption that the probabilities reflect the observed proportions of the total landscape area occupied by the corresponding events (i.e., fire incident or no fire) and conditions (i.e., classes) of predictor variables.

Any locus identified as being significantly associated with one or more of these classes of predictor variables was then included in a multivariate analysis to determine the combination of predictor variables that best explained observed genetic variation.

The percent of genetic variation accounted for by the microsatellite genotype data and the mtDNA data for three different classes of predictor variables are shown in Table  2.> -wrap-foot> aModel includes only geographic distance between sample locations as a predictor variable.

The complexity of a specific class of predictors, i.e. class size and statistical regression affects the predictor convergence guarantee to the desired redundancy loss bound (see redundancy-capacity theorem [20,23,28,31]).

This class of predictors uses a series of analytical tools (called modules) to assign likelihoods for a given protein to be located at a specific compartment.

In PM10 models, distance to the nearest road of each road class was the most important class of predictors, likely representing traffic, an important local source of particulate matter (U.S. EPA 2004).