blockmodeling (version 0.1.9)

check.these.par: Computes the value of a criterion function for a given network and a set of partitions

Description

The function computes the value of a criterion function for a given network and a set of partitions for Generalized blockmodeling. (<U+017D>iberna, 2006) based on other parameters (see below and crit.fun).

Usage

check.these.par(M, partitions, approach, return.err = TRUE,
  save.initial.param = TRUE, force.fun = NULL, ...)

Arguments

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.

partitions

A list of partitions. 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.

approach

One of the approaches described in <U+017D>iberna (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.

return.err

Should the error for each evaluated partition be returned

save.initial.param

Should the inital parameters (approach,...)

force.fun

Select the function used to evaluate the network and a partition. This should be used only in exterem cases. Otherwise, the appropriate function is selected (generated) besed on the input parameters.

…

Argumets to gen.crit.fun see crit.fun for description. Some are required!!!

Value

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

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.

...

Warning

This function is usually used to check all possible partitions. If the number of partitions is large (several 1000), this can be extremly time demanding. It is advaisable to firtst time the function on a smaller subset.

References

<U+017D>IBERNA, Ale<U+0161> (2006): Generalized Blockmodeling of Valued Networks. Social Networks, Jan. 2007, vol. 29, no. 1, 105-126. http://dx.doi.org/10.1016/j.socnet.2006.04.002.

<U+017D>IBERNA, Ale<U+0161>. 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.

DOREIAN, Patrick, BATAGELJ, Vladimir, FERLIGOJ, Anu<U+0161>ka (2005): Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press, 2005. XV, 384 p., ISBN 0-521-84085-6.

See Also

crit.fun, opt.par, opt.these.par, nkpartitions, plot.check.these.par

Examples

# NOT RUN {
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)

#computation of criterion function with the correct partition
nkpar(n=n, k=length(tclu)) #computing the number of partitions
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<-check.these.par(M=net,partitions=all.par,approach="ss",
   blocks="com")
plot(res) #we get the original partition
# }