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All predictors will be checked for collinearity.
All continuous predictors will be centred about their means.
Only highly likely predictors will be included in this process.
Only the strongest predictors will be tested as confounding variables.
All candidate predictors will be included in our prognostic model.
For each prediction model a different selection of candidate predictors will be used.
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This predictor will be referred to as "the trivial predictor" and it measures the amount of stationarity in the mobility sequence.
The predictor will be univariate in that it will only consider the historical sensor readings to adjust the parameters of the model.
Interface predictors often disagree strongly with each other; in most cases, at least one predictor will be correct but it is not possible to tell which one [4].
For Aim 3, predictor variables will be assessed for collinearity and each predictor will be assessed using a univariate model.
It is known that the performance of AMPs predictor will be affected by homologous sequences in the datasets.
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