Functions to create functions that filter potential predictive features using statistics that do not access class labels.
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A real number, the level above which features with this statistic should be retained and below which should be discarded.
Following the usual conventions introduced from the world of gene expression microarrays, a typical data matrix is constructed from columns representing samples on which we want to make predictions amd rows representing the features used to construct the predictive model. In this context, we define a filter to be a function that accepts a data matrix as its only argument and returns a logical vector, whose length equals the number of rows in the matrix, where 'TRUE' indicates features that should be retrained. Most filtering functions belong to parametrized families, with one of the most common examples being "retain all features whose mean is above some pre-specified cutoff". We implement this idea using a set of function-generating functions, whose arguments are the parameters that pick out the desired member of the family. The return value is an instantiation of a particular filtering function. The decison to define things this way is to be able to apply the methods in cross-validation (or other) loops where we want to ensure that we use the same filtering rule each time.
Each of the seven functions described here return a filter function,
f, that can be used by code that basically looks like
logicalVector <- filter(data).
Kevin R. Coombes <email@example.com>
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