YoudenIndex: Youden's Index
In synRNASeqNet: Synthetic RNA-Seq Network Generation and Mutual Information Estimates
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
The Youden's Index evaluates the algorithm's ability to avoid failure.
Usage
1
YoudenIndex(resTable)
Arguments
resTable
the data.frame generated from the performanceNET (or one of the row
of the matrix generated from the performanceIndex) function.
Value
The YoudenIndex function returns the value of the Youden's Index for a
given predicted adjacency matrix.
Author(s)
Luciano Garofano lucianogarofano88@gmail.com, Stefano Maria Pagnotta, Michele Ceccarelli
References
Sokolova M., Japkowicz N., Szpakowicz S. (2006). Beyond Accuracy, F-Score and
ROC: A Family of Discriminant Measures for Performance Evaluation. Lecture
Notes in Computer Science Volume 4304, 2006, pp 1015-1021.
See Also
Likelihoods,
DiscriminantPower
Examples
1
2
3
4
5
6
7
8
9
10
simData <- simulatedData(p = 10, n = 20, mu = 100, sigma = 0.25,
ppower = 0.73, noise = FALSE)
counts <- simData$counts
adjMat <- simData$adjMat
miML <- parMIEstimate(counts, method = "ML", unit = "nat", nchips = 2)
miML <- aracne.a(miML, eps = 0.5)
valML <- performanceNET(miML, adjMat)
YI <- YoudenIndex(valML)
synRNASeqNet documentation built on May 2, 2019, 6:01 a.m.
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Description Usage Arguments Value Author(s) References See Also Examples
Description
The Youden's Index evaluates the algorithm's ability to avoid failure.
Usage
1 | YoudenIndex(resTable)
|
Arguments
resTable |
the data.frame generated from the |
Value
The YoudenIndex function returns the value of the Youden's Index for a
given predicted adjacency matrix.
Author(s)
Luciano Garofano lucianogarofano88@gmail.com, Stefano Maria Pagnotta, Michele Ceccarelli
References
Sokolova M., Japkowicz N., Szpakowicz S. (2006). Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. Lecture Notes in Computer Science Volume 4304, 2006, pp 1015-1021.
See Also
Likelihoods,
DiscriminantPower
Examples
1 2 3 4 5 6 7 8 9 10 | simData <- simulatedData(p = 10, n = 20, mu = 100, sigma = 0.25,
ppower = 0.73, noise = FALSE)
counts <- simData$counts
adjMat <- simData$adjMat
miML <- parMIEstimate(counts, method = "ML", unit = "nat", nchips = 2)
miML <- aracne.a(miML, eps = 0.5)
valML <- performanceNET(miML, adjMat)
YI <- YoudenIndex(valML)
|
R Package Documentation
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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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