F1.kdiffnet: computes F1 score for jointnet result
In JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application
Description
Usage
Arguments
Examples
Description
computes F1 score for jointnet result
Usage
1
2
Arguments
result
output generated from any one of the jointnet algorithms
simulatedgraphs
$simulatedgraphs from function simulation()
...
unused
Examples
1
2
3
4
5
6
7
8
library(JointNets)
simulationresult = simulation(p = 20, n = c(100,100))
truth = simulationresult$simulatedgraphs
result = kdiffnet(simulationresult$simulatedsamples[[1]],
simulationresult$simulatedsamples[[2]],
W = matrix(1,20,20), g = rep(0,20),epsilon = 0.2,
lambda = 0.4,covType = "cov")
F1(result,truth)
JointNets documentation built on July 30, 2019, 1:02 a.m.
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Description Usage Arguments Examples
Description
computes F1 score for jointnet result
Usage
1 2 |
Arguments
result |
output generated from any one of the jointnet algorithms |
simulatedgraphs |
$simulatedgraphs from function simulation() |
... |
unused |
Examples
1 2 3 4 5 6 7 8 | library(JointNets)
simulationresult = simulation(p = 20, n = c(100,100))
truth = simulationresult$simulatedgraphs
result = kdiffnet(simulationresult$simulatedsamples[[1]],
simulationresult$simulatedsamples[[2]],
W = matrix(1,20,20), g = rep(0,20),epsilon = 0.2,
lambda = 0.4,covType = "cov")
F1(result,truth)
|
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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