abnormality - Measure a Subject's Abnormality with Respect to a Reference
Population
Contains the functions to implement the methodology and
considerations laid out by Marks et al. in the manuscript
Measuring Abnormality in High Dimensional Spaces: Applications
in Biomechanical Gait Analysis. As of 2/27/2018 this paper has
been submitted and is under scientific review. Using
high-dimensional datasets to measure a subject’s overall level
of abnormality as compared to a reference population is often
needed in outcomes research. Utilizing applications in
instrumented gait analysis, that article demonstrates how using
data that is inherently non-independent to measure overall
abnormality may bias results. A methodology is introduced to
address this bias to accurately measure overall abnormality in
high dimensional spaces. While this methodology is in line with
previous literature, it differs in two major ways.
Advantageously, it can be applied to datasets in which the
number of observations is less than the number of
features/variables, and it can be abstracted to practically any
number of domains or dimensions. After applying the proposed
methodology to the original data, the researcher is left with a
set of uncorrelated variables (i.e. principal components) with
which overall abnormality can be measured without bias.
Different considerations are discussed in that article in
deciding the appropriate number of principal components to keep
and the aggregate distance measure to utilize.
