Description Usage Arguments Value Warning Author(s) References See Also Examples
The function optimizes a set partitions based on the value of a criterion function (see critFunC
for details on the criterion function) 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 neighborhood 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).
A list of paritions can or the number of clusters and a number of partitions to generate can be specified (optParC
).
1 2 3 4 5 6 7 8 9 10 | optRandomParC(M, k, rep, save.initial.param = TRUE,
save.initial.param.opt = FALSE, deleteMs = TRUE,
max.iden = 10, switch.names = NULL, return.all =
FALSE, return.err = TRUE, seed = NULL, RandomSeed =
NULL, parGenFun = genRandomPar, mingr = NULL, maxgr =
NULL, addParam = list(genPajekPar = TRUE, probGenMech
= NULL), maxTriesToFindNewPar = rep * 10, skip.par =
NULL, useOptParMultiC = FALSE, useMulti =
useOptParMultiC, printRep = ifelse(rep <= 10, 1,
round(rep/10)), n = NULL, nCores = 1, ...)
|
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. |
k |
The number of clustrs used in generation of partitions. |
rep |
The number of repetitions/different starting partitions to check. |
save.initial.param |
Should the inital parameters ( |
save.initial.param.opt |
Should the inital parameters( |
deleteMs |
Delete networks/matrices from results of to save space. |
max.iden |
Maximum number of results that should be saved (in case there are more than |
switch.names |
Should partitions that only differ in group names be considert equal. |
return.all |
If |
return.err |
Should the error for each optimized partition be returned. |
seed |
Optional. The seed for random generation of partitions. |
RandomSeed |
Optional. integer vector, containing the random number generator.It is only looked for in the user's workspace. |
parGenFun |
The function (object) that will generate random partitions. The default fuction is |
mingr |
Minimal alowed group size |
maxgr |
Maximal alowed group size |
addParam |
A list of additional parameters for function specified above. In the usage section they are specified for the default function |
genPajekPar |
Should the partitions be generated as in Pajek. |
probGenMech |
Should the probabilities for different mechanizems for specifying the partitions be set. If |
maxTriesToFindNewPar |
The maximum number of partition try when trying to find a new partition to optimize that was not yet checked before - the default value is |
skip.par |
The partitions that are not allowed or were already checked and should therfire be skiped. |
useOptParMultiC |
For backward compatibility. May be removed soon. See next argumetent. |
useMulti |
Which version of local search should be used. Default is currently |
printRep |
Should some information about each optimization be printed. |
n |
the number of units by "modes". It is used only for generating random partitions. It has to be set only if there are more than two modes or if there are two modes, but the matrix representing the network is one mode (both modes are in rows and columns). |
nCores |
Number of cores to be used. Value |
... |
Argumets passed to other functions, see |
M |
The matrix of the network analyzed |
res |
If |
best |
A list of results from |
err |
If |
nIter |
The vector of number of iterations used - one value for each starting partition that was optimized. It can show that |
checked.par |
If selected - A list of checked parititions. If |
call |
The call used to call the function. |
initial.param |
If selected - The inital parameters used. |
It should be noted that the time complexity of package blockmodeling is increasing with the number of units and the number of clusters (due to its algorithm). Therefore the analysis of network with more than 100 units can take a lot of time (from a few hours to few days).
Aleš Žiberna
ŽIBERNA, Aleš (2007): 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.
ŽIBERNA, Aleš. 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, Anuška (2005): Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press, 2005. XV, 384 p., ISBN 0-521-84085-6.
BATAGELJ, Vladimir, MRVAR, Andrej (2006): Pajek 1.11, http://vlado.fmf.uni-lj.si/pub/networks/pajek/ (accessed January 6, 2006).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | n <- 8 # if larger, the number of partitions increases dramatically
# 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
# optimizing one partition
res <- optParC(M = net,
clu = all.par[[sample(1:length(all.par), size = 1)]],
approaches = "hom", homFun = "ss", blocks = "com")
plot(res) # Hopefully we get the original partition
# optimizing 10 random chosen partitions with optRandomParC
res <- optRandomParC(M = net, k = 2, rep = 10,
approaches = "hom", homFun = "ss", blocks = "com")
plot(res) # Hopefully we get the original partition
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