Visualisation of the ROC based threshold method for estimating networks,
implemented by the
Output from 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
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
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# 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)
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