sliceQuality: Evaluate Several Characteristics of Slices from a CSS

In cssTools: Cognitive Social Structure Tools

Description

Given a fully observed CSS, this function evaluates the quality of each slice by comparing them with the true network obtained by LAS intersection.

Usage

sliceQuality(d)

Arguments

d	A CSS in cssTools package format.

Details

A common way of defining a true network for a given CSS is the LAS intersection (see, for example, Siciliano et. al. 2012, or Krackhardt, 1987). For a given CSS, the function sliceQuality first computes the true network by LAS intersection, and then compares each slice with the true network. The considered quantities are matching zeros, matching ones, type 1 errors, type 2 errors, S_{14} similarity index, error proportion and correlation.

Value

trueNetwork

The true network obtained by LAS intersection method.

sliceQuality

A table summarizing the quality of each CSS slice in rows. Columns indicate A (matching zeros), B (0 in CSS slice, 1 in true matrix, i.e., type 2 error), C (1 in CSS slice, 0 in true network, i.e., type 1 error) D (matching ones), s14 (S_{14} similarity index between the CSS slice and the true nework), errorProp (proportion of unmatching cells), and correlation (correlation between the CSS slice and the true network computed by the gcor function in the sna package).

Author(s)

Deniz Yenigun, Gunes Ertan, Michael Siciliano

References

Krackhardt, D. (1987). Cognitive social structures. Social Networks 9, 109-134. http://dx.doi.org/10.1016/0378-8733(87)90009-8

M.D. Siciliano, D. Yenigun, G. Ertan (2012). Estimating network structure via random sampling: Cognitive social structures and adaptive threshold method. Social Networks, Vol. 34, No. 4, 585-600. http://dx.doi.org/10.1016/j.socnet.2012.06.004

See Also

s14

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

# Compute the quality of CSS slices
sliceQuality(d)

Example output

Loading required package: sna
Loading required package: statnet.common

Attaching package: 'statnet.common'

The following object is masked from 'package:base':

    order

Loading required package: network
network: Classes for Relational Data
Version 1.13.0.1 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.

sna: Tools for Social Network Analysis
Version 2.4 created on 2016-07-23.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.

$trueNetwork
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    0    0    0    0
[2,]    0    1    1    0    1
[3,]    1    1    1    0    0
[4,]    0    0    0    1    1
[5,]    0    0    0    1    1

$sliceQuality
      A B C  D       s14 errorProp correlation
[1,] 11 3 3  8 0.5129870       0.3   0.2857143
[2,] 11 3 3  8 0.5129870       0.3   0.2857143
[3,] 13 3 1  8 0.6782343       0.2   0.4909903
[4,]  9 1 5 10 0.5592589       0.3   0.4364358
[5,] 11 3 3  8 0.5129870       0.3   0.2857143

cssTools documentation built on May 2, 2019, 1:26 p.m.