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After the LR model has been built using a forward stepwise selection procedure for the choice of a subset of predictor variables x i (i = 1, 2,..., d), each continuous predictor is categorized using a locally weighted scatterplot procedure to subjectively identify cut-off points on the basis of training data.
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All but one FD predictor was categorized in binary form for analysis purposes; namely, primary language (English not first language =1), requiring care (1), receiving community support (1), education (primary only =1, primary and secondary only =2, any tertiary =3), and using a gait aid (1).
The predictors were categorized into individual characteristics, information/education/training, design to support worker needs, safety climate, competing goals, and problems with rules.
Predictors were categorized as potentially modifiable preventive factors (HR<1.0), potentially modifiable risk factors (HR>1.0), and patient characteristics.
The predictors were categorized in the same way that Sartorius et al. [ 5] described when they developed the MGAP.
These predictors were categorized into groups representing the size of the pathway (i.e., amount of genes/SNPs/SNP-gene ratio on each pathway).
In order to account for potential non-linearity of associations, predictors were categorized according to previously used cut-off points in this population.
The variable has a one-to-one relationship with the finite space state coded by : one specific value of represents a specific combination of the values of the original predictors, that is, a "bin" into which the data is categorized.
CFS is categorized aa filter.
The site is categorized.
The track is categorized as a ballad.
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