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)
``` |

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