Description Usage Arguments Details Author(s) References See Also Examples
Visualisation of the ROC based threshold method for estimating networks,
implemented by the rtm
function.
1 | rtmPlot(rtmOutput)
|
rtmOutput |
Output from the function |
The function rtm
uses the density weighted ROC based threshold method (RTM) of Yenigun et. al. (2016)
for estimating networks from a random sample of CSS slices. The output from rtm
is
visualized by the function rtmPlot
, which displays the ROC curve, as well as the
type 1 and type 2 error counts for each threshold value.
Deniz Yenigun, Gunes Ertan, Michael Siciliano
D. Yenigun, G. Ertan, M.D. Siciliano (2016). Omission and commission errors in network cognition and estimation using ROC curve. arXiv:1606.03245 [stat.CO] https://arxiv.org/abs/1606.03245
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # Load the highTechManagers data given in cssTools package
data(highTechManagers)
# There are 21 CSS slices in the complete data
# Suppose we only observed the 10 slices with the following indexes
sampled=c(2,4,5,8,9,10,11,14,18,19)
# Then the observed data is the following
dSampled=highTechManagers[,,sampled]
# Apply the ROC based threshold method to estimate the network
y=rtm(dSampled,sampled)
# Now plot the ROC curve and the error types for various threshold values
rtmPlot(y)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.