| Name : R-abnormality
| |
| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.13
| Date : 2024-06-14 11:05:11
|
| Group : Development/Libraries/Other
| Source RPM : R-abnormality-0.1.0-lp153.2.13.src.rpm
|
| Size : 0.02 MB
| |
| Packager : (none)
| |
| Summary : Measure a Subject\'s Abnormality with Respect to a Reference Population
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
