rtm {cssTools}

Estimate a Network Using the ROC Based Threshold Method

Description

Estimate a network of interest by aggregating the sampled CSS slices using the ROC based threshold method.

Usage

rtm(d, sampled)

Arguments

d

Sampled CSS slices in cssTools package format.

sampled

A vector indicating which network individuals are sampled.

Details

Given a random sample of observed CSS slices, the rtm function uses the density weighted ROC based threshold method (RTM) of Yenigun et. al. (2016) to aggregate the observed slices, and provides an estimate for the network of interest. Slice densities are computed by the gden function in the sna package.

Value

estimatedNetwork

An estimate of the network of interest.

type1Error

Estimated type 1 error rate at the optimum threshold returned by the density weighted ROC method.

type2Error

Estimated type 2 error rate at the optimum threshold returned by the density weighted ROC method.

threshold

The optimum threshold value.

details

A table giving the details of the density weighted ROC method.Columns indicate the threshold, type 1 error (false positive rate), type 2 error, true positive rate (1 - type 2 error), type 1 error count, type 2 error count, and distance.

Author(s)

Deniz Yenigun, Gunes Ertan, Michael Siciliano

References

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

See Also

atm, ftm

Examples

# Consider the example in Siciliano et. al. (2012),
# a network with five actors A, B, C, D, E
sA=matrix(c(0,0,1,0,1,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0),5,5)
sB=matrix(c(0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0),5,5)
sC=matrix(c(0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0),5,5)
sD=matrix(c(0,0,1,0,1,0,0,1,1,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0),5,5)
sE=matrix(c(0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0),5,5)
d=array(dim=c(5,5,5))
d[,,1]=sA
d[,,2]=sB
d[,,3]=sC
d[,,4]=sD
d[,,5]=sE

# Suppose you randomly sampled A, D, and E
sampled=c(1,4,5)

# Then all you have is the following three sampled slices of A, D and E
dSampled=d[,,sampled]

# We can combine these slices as follows, 
# which gives an estimate of the complete network
rtm(dSampled,sampled)