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Because of the excessively large number of input variables available, and the lack of any automated procedure for variable selection, it was decided to reduce the number of inputs to the neural network models by taking principal components of the 20 variables and alternatively by using the variable combinations that had been selected in the corresponding discriminant analyses.
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By using the final combination variable, all combinations of clinical information (pain/side-flexion variable) and psychosocial information were tabulated in relation to risk groups (Table 4).
Moreover, predicting or estimating the dependent variable using the optimal combination of multiple independent variables is more effective and realistic than using only one independent variable (Hu et al. 2013).
We also try an alternative specification of the auxiliary distribution used as a part of the initial values problem by using the combinations of the variables.
The effect of infection intensity on amplification success was also supported by experimental tests using variable combinations of pure bacterial and host DNA; as host DNA concentrations increase relative to that of the bacteria, pathogen detection is considerably reduced (results not shown).
The 3-way array of state variable data (observations x outputs x time points) was first used as regressor in a test set validated N-way HC-PLSR using the parameter combinations as response-variables (inverse metamodelling).
Treatments used were variable combinations of surgical resection (7%), radiation therapy (34%), and chemotherapy (80%).
Three predictive models (one for variants in both BRCA1 and BRCA2 and one for each of these separately) were constructed using the best combination of variables that distinguished between deleterious and neutral variants.
Due to the lack of a specific single surface marker protein for MSC, we used variable combinations of different markers for flow cytometric analysis to characterize the isolated MSC and CPC.
Using each of the four approaches (WC1, WC2, CLR and, ULR), we estimated the effects of intensity and duration of occupational asbestos exposure, the age at first exposure, and time since last exposure, using the same combination of quantitative exposure variables as in Models 1 3 of the simulation study.
Better information comes available for choosing optimised implementation strategies where organizational and capacity variables are important using the combination of simulation models and an experimental design.
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