estimateMSVmetrics-methods: Estimates the GPD and SPOmulti-source variability metrics and...
In carsaesi/EHRsourceVariability: Measurement and visualization of biomedical data heterogeneity across data sources
Description
Usage
Arguments
Value
Description
Estimates the GPD and SPOmulti-source variability metrics and the corresponding source projection vertices from a matrix of probability distributions of different data sources
Usage
1
estimateMSVmetrics(msvMetrics, nBySource, idSource)
Arguments
probabilities
m-by-n matrix containing the probability mass of n data sources on m distribution bins
Value
A list containing the following results:
GPD the value of the Global Probabilistic Deviation metric, where 0 means equal distributions and 1 means non-overlapping distributions
SPOs the values of Source Probabilistic Outlyingness for each data source, where 0 means equal to central tendency and 1 completely non-overlapping
Vertices a n-by-(n-1) matrix containing the coordinates of each data source in the projected probabilistic space conserving their dissimilarities, e.g, for 3D projections the 3 first columns can be used
carsaesi/EHRsourceVariability documentation built on June 6, 2019, 7:52 a.m.
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Description Usage Arguments Value
Description
Estimates the GPD and SPOmulti-source variability metrics and the corresponding source projection vertices from a matrix of probability distributions of different data sources
Usage
1 | estimateMSVmetrics(msvMetrics, nBySource, idSource)
|
Arguments
probabilities |
m-by-n matrix containing the probability mass of n data sources on m distribution bins |
Value
A list containing the following results: GPD the value of the Global Probabilistic Deviation metric, where 0 means equal distributions and 1 means non-overlapping distributions SPOs the values of Source Probabilistic Outlyingness for each data source, where 0 means equal to central tendency and 1 completely non-overlapping Vertices a n-by-(n-1) matrix containing the coordinates of each data source in the projected probabilistic space conserving their dissimilarities, e.g, for 3D projections the 3 first columns can be used
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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