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The predictor will be univariate in that it will only consider the historical sensor readings to adjust the parameters of the model.
This predictor will be referred to as "the trivial predictor" and it measures the amount of stationarity in the mobility sequence.
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].
It is known that the performance of AMPs predictor will be affected by homologous sequences in the datasets.
The mapping of these regions, produced by our predictor, will be of importance for future genome evolution and function studies.
For Aim 3, predictor variables will be assessed for collinearity and each predictor will be assessed using a univariate model.
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All predictors will be checked for collinearity.
All candidate predictors will be included in our prognostic model.
Only highly likely predictors will be included in this process.
Only the strongest predictors will be tested as confounding variables.
All continuous predictors will be centred about their means.
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