blockmodeling (version 0.1.7)

REGE: REGE - Algorithms for compiting (dis)similarities in terms of regular equivalnece.

Description

REGE - Algorithms for compiting (dis)similarities in terms of regular equivalnece (White and Reitz, 1983): REGE, REGE.for - Classical REGE or REGGE, as also implemented in Ucinet. Similarities in terms of regular equivalnece are computed. The REGE.for is a wraper for calling the FORTRAN subrutine writen by White (1985a), modified to be called by R. The REGE does the same, however it is written in R. The functions with and without ".for" differ only in wheater they are implemeted in R of FORTRAN. Needless to say, the funcitons implemeted in FORTRAN are much faster. REGE.ow, REGE.ow.for - The above function, modified so that a best match is searhed for for each arc speleratly (and not for both arcs, if they exist, together) REGE.nm.for - REGE or REGGE, modified to to use row and column normalited matrices instead of the original matrix. REGE.ownm.for - The above function, modified so that a best match is searhed for for each arc speleratly (and not for both arcs, if they exist, together) REGD.for - REGD or REGDI, a dissimilarity version of the classical REGE or REGGE. Dissimilarities in terms of regular equivalnece are computed. The REGD.for is a wraper for calling the FORTRAN subrutine writen by White (1985b), modified to be called by R. REGE.FC - Acctually an erlier version of REGE. The diference is in the denominator. See Žiberna (2006) for details. REGE.FC.ow - The above function, modified so that a best match is searhed for for each arc speleratly (and not for both arcs, if they exist, together) other - still in testing stage

Usage

REGE(M, E = 1, iter = 3, until.change = TRUE, use.diag = TRUE)
REGE.for(M, iter = 3, E = 1)
REGE.nm.for(M, iter = 3, E = 1)
REGE.ow(M, E = 1, iter = 3, until.change = TRUE, use.diag = TRUE)
REGE.ow.for(M, iter = 3, E = 1)
REGE.ownm.for(M, iter = 3, E = 1)
REGD.for(M, iter = 3, E = 0)
REGD.ow.for(M, iter = 3, E = 0)
REGE.FC(M, E = 1, iter = 3, until.change = TRUE, use.diag = TRUE,
   normE = FALSE)
REGE.FC.ow(M, E = 1, iter = 3, until.change = TRUE,
   use.diag = TRUE, normE = FALSE)
REGD.ne.for(M, iter = 3, E=0)
REGD.ow.ne.for(M, iter = 3, E = 0)
REGE.ne.for(M, iter = 3, E=1)
REGE.nm.diag.for(M, iter = 3, E=1)
REGE.nm.ne.for(M, iter = 3, E=1)
REGE.ow.ne.for(M, iter = 3, E=1)
REGE.ownm.diag.for(M, iter = 3, E=1)
REGE.ownm.ne.for(M, iter = 3, E=1)

Arguments

M

Matrix or a 3 dimensional array representing the network. The third dimension allows for several relations to be analyzed.

E

Initial (dis)similarity in terms of regular equivalnece.

iter

The desired number of itetations

until.change

Should the iterations be stop when no change occours

use.diag

Should the diagonal be used. If FALSE, all diagonal elements are set to 0.

normE

Should the equivalence matrix be normalized after each iteration?

Value

• E
  A matrix of (dis)similarities in terms of regular equivalnece
• Eall
  An array of (dis)similarity matrices in terms of regular equivalnece, each third dimmension represets one iteration. For ".for" functions, only the initial and the final (dis)similarities are returned.
• M
  Matrix or a 3 dimensional array representing the network used in the call.
• iter
  The desired number of itetations
• use.diag
  Should the diagonal be used - for functions implemeted in R only.
• ...

References

ŽIBERNA, Aleš{ZIBERNA, Ales}. Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. J. math. sociol., 2008, vol. 32, no. 1, 57-84. http://www.informaworld.com/smpp/content?content=10.1080/00222500701790207. White, D. R., K. P. Reitz (1983): "Graph and semigroup homomorphisms on networks of relations". Social Networks, 5, p. 193-234. White, Douglas R.(1985a): DOUG WHITE'S REGULAR EQUIVALENCE PROGRAM. http://eclectic.ss.uci.edu/~drwhite/REGGE/REGGE.FOR (12.5.2005). White, Douglas R.(1985b): DOUG WHITE'S REGULAR DISTANCES PROGRAM. http://eclectic.ss.uci.edu/~drwhite/REGGE/REGDI.FOR (12.5.2005). White, Douglas R.(2005): REGGE (web page). http://eclectic.ss.uci.edu/~drwhite/REGGE/ (12.5.2005).

See Also

sedist, crit.fun, opt.par, plot.mat

Examples

n<-20
net<-matrix(NA,ncol=n,nrow=n)
clu<-rep(1:2,times=c(5,15))
tclu<-table(clu)
net[clu==1,clu==1]<-0
net[clu==1,clu==2]<-rnorm(n=tclu[1]*tclu[2],
   mean=4,sd=1)*sample(c(0,1),
   size= tclu[1]*tclu[2],replace=TRUE,prob=c(3/5,2/5))
net[clu==2,clu==1]<-0
net[clu==2,clu==2]<-0

D<-REGE.for(M=net)$E #any other REGE function can be used
plot.mat(net, clu=cutree(hclust(d=as.dist(1-D),method="ward"),
   k=2))
#REGE returns similarities, which have to be converted to
#disimilarities

res<-opt.random.par(M=net,k=2,rep=10,approach="ss",blocks="reg",
   FUN="max")
plot(res) #Hopefully we get the original partition