opt.par {blockmodeling} | R Documentation |
The function optimizes a partition based on the value of a criterion function (see crit.fun
) for a given network and blockmodel for Generalized blockmodeling (Žiberna, 2006) based on other parameters (see below).
The optimization is done through local optimization, where the neighbourhood of a partition includes all partitions that can be obtained by moving one unit from one cluster to another or by exchanging two units (from different clusters).
opt.par(M, clu, maxiter = 50, m = NULL, approach, trace.iter = FALSE, switch.names = TRUE, save.initial.param = TRUE, skip.par = NULL, save.checked.par = !is.null(skip.par), merge.save.skip.par = all(!is.null(skip.par), save.checked.par), check.skip = "never", ...)
M |
A matrix representing the (usually valued) network. For now, only one-relational networks are supported. The network can have one or more modes (diferent kinds of units with no ties among themselvs. If the network is not two-mode, the matrix must be square. |
clu |
A partition. Each unique value represents one cluster. If the nework is one-mode, than this should be a vector, else a list of vectors, one for each mode |
maxiter |
Maxsimum number of iterations allowed |
m |
Suficient value for individual cells for valued approach. Can be a number or a character string giving the name of a function. Set to "max" for implicit approach. |
approach |
One of the approaches described in Žiberna (2006). Possible values are: "bin" - binary blockmodeling, "val" - valued blockmodeling, "imp" - implicit blockmodeling, "ss" - sum of squares homogenity blockmodeling, and "ad" - absolute deviations homogenity blockmodeling. |
trace.iter |
Should the result of each iteration (and not only of the best one) be saved |
switch.names |
Should partitions that differ only in diferent names of positions be treated as different. It should be set to TRUE only if a asymetric blockmodel via BLOCKS is specified. |
save.initial.param |
Should the inital parameters (approach ,...) be saved |
skip.par |
The partitions that are not allowed or were already checked and should therfire be skiped. |
save.checked.par |
Should the checked partitions be saved. For example, so that they can be used in the next call as skip.par |
merge.save.skip.par |
Should the checked partitions be merged with skiped ones? |
check.skip |
When should the check be preformed: "all" - before every call to 'crit.fun' "iter" - at the end of eack iteratiton "never" - never |
use.for.opt |
Should FORTRAN function be used for optimization if possible. If FORTRAN function is used, the speed is dramatically increast, however some the output is slightly different and the plotting function might not work. |
... |
Argumets passed to other functions, see crit.fun |
M |
The matrix of the network analyzed |
best |
A list of results from crit.fun.tmp with the same elements as the result of crit.fun , only without M |
iter |
A list of resoults the same as best - one best for each iteration |
err |
If selected - The vector of errors or inconsistencies of the emplirical network with the ideal network for a given blockmodel (model,approach,...) and parititions |
call |
The call used to call the function. |
initial.param |
If selected - The inital parameters used. |
checked.par |
If selected - A list of checked parititions. If merge.save.skip.par is TRUE , this list also includs the partitions in skip.par . |
...
This function can be extremly slow. The time complexity is incrising with the number od units and the number of clusters. It is advaisable to firtst time the function on a smaller network.
Aleš Žiberna
ŽIBERNA, Aleš. Generalized blockmodeling for valued networks. V: SUNBELT XXV : Program and abstracts. Redondo Beach, 2005, p. 79. http://www.socsci.uci.edu/~ssnconf/conf/SunbeltXXVProgram.pdf.
DOREIAN, Patrick, BATAGELJ, Vladimir, FERLIGOJ, Anuška. Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press, 2005. XV, 384 p., ISBN 0-521-84085-6.
crit.fun
,check.these.par
,opt.random.par
,opt.these.par
,plot.opt.par
n<-8 #if larger, the number of partitions increases dramaticaly, as does if we increase the number of clusters net<-matrix(NA,ncol=n,nrow=n) clu<-rep(1:2,times=c(3,5)) tclu<-table(clu) net[clu==1,clu==1]<-rnorm(n=tclu[1]*tclu[1],mean=0,sd=1) net[clu==1,clu==2]<-rnorm(n=tclu[1]*tclu[2],mean=4,sd=1) net[clu==2,clu==1]<-rnorm(n=tclu[2]*tclu[1],mean=0,sd=1) net[clu==2,clu==2]<-rnorm(n=tclu[2]*tclu[2],mean=0,sd=1) #we select a random parition and then optimise it all.par<-nkpartitions(n=n, k=length(tclu)) #forming the partitions all.par<-lapply(apply(all.par,1,list),function(x)x[[1]]) # to make a list out of the matrix res<-opt.par(M=net,clu=all.par[[sample(1:length(all.par,size=1)]],approach="ss",blocks="com") plot(res) #Hopefully we get the original partition