block.fit: Block Fit
In zalmquist/networkMethods: Network Methods

Description Usage Arguments Value Author(s) Examples

block.fit returns an object of class "blockmodel."

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block.fit(g, model, measure = c("pearson", "negdist"), diag = FALSE,
  iter = 3000, temp.init = 10, cool = 0.995, hill.climb.refine = TRUE,
  seed = NULL, verbose = TRUE)

`g`	a network to be analyzed
`model`	a vector or matrix specifying the blockmodel to be fit. If passed as a vector, the argument should be given column-wise. For instance, two-class blockmodels are fit by passing a vector of the form c(b11,b21,b12,b22), where bij is the value of the i,j cell in the blockmodel. Usually, these values are 1 or 0, although NAs are allowed (and have the effect of removing the indicated cells from consideration when evaluating goodness of fit).
`measure`	"pearson" for the standard correlation coefficent (may produce warnings, which are harmless) or "negdist" for the inverse absolute distance.
`diag`	should the diagonal be included?
`iter`	number of annealing iterations to employ.
`temp.init`	initial temperature for the annealer.
`cool`	cooling factor for the annealer.
`hill.climb.refine`	should the annealing solution be further refined using
`seed`	an optional vector of initial group memberships.
`verbose`	should the function provide diagnostic output?
`hill-climbing?`	(Can be slow, but guarantees convergence to a local optimum.) optimum.

An object of class "blockmodel," with the additional list element block.gof. (Note that this object can be printed, etc. using the standard methods for this class.)

Carter T. Butts, University of California, Irvine

require(sna)
  require(network)
  data(kaptail.ins)
  plot(kaptail.ins)
# Let's try fitting some blockmodels.  Here are several variants on C/P:
kb1<-block.fit(kaptail.ins,c(1,1,1,0))   # Core w/in,out ties
kb2<-block.fit(kaptail.ins,c(1,1,0,0))   # Core w/in ties
kb3<-block.fit(kaptail.ins,c(1,0,1,0))   # Core w/out ties
kb4<-block.fit(kaptail.ins,c(1,0,0,0))   # Isolated core
kb5<-block.fit(kaptail.ins,c(1,NA,NA,0)) # Ignore core/periphery interactions

# Examine the output directly
kb1                                       # These are "blockmodel" objects
summary(kb2)

# Plot each model as a "blocked" data matrix
lab<-kb1$block.membership[kb1$order.vector]
plot.sociomatrix(kb1$blocked.data,labels=list(lab,lab))
lab<-kb2$block.membership[kb2$order.vector]
plot.sociomatrix(kb2$blocked.data,labels=list(lab,lab))
lab<-kb3$block.membership[kb3$order.vector]
plot.sociomatrix(kb3$blocked.data,labels=list(lab,lab))
lab<-kb4$block.membership[kb4$order.vector]
plot.sociomatrix(kb4$blocked.data,labels=list(lab,lab))
lab<-kb5$block.membership[kb5$order.vector]
plot.sociomatrix(kb5$blocked.data,labels=list(lab,lab))

# Plot the original data, with vertices colored by block (black=1, red=2)
plot(kaptail.ins,vertex.col=kb1$block.membership)
plot(kaptail.ins,vertex.col=kb2$block.membership)
plot(kaptail.ins,vertex.col=kb3$block.membership)
plot(kaptail.ins,vertex.col=kb4$block.membership)
plot(kaptail.ins,vertex.col=kb5$block.membership)

# Let's try another example -- this one (from Thuroff) is undirected
data(thuroff.int)
plot(thuroff.int)

# Fit various undirected blockmodels
tb1<-block.fit(thuroff.int,c(1,1,1,0))    # Core w/in,out ties
tb2<-block.fit(thuroff.int,c(1,0,0,0))    # Isolated core
tb3<-block.fit(thuroff.int,c(1,NA,NA,0))  # Ignore core/periphery relations
tb4<-block.fit(thuroff.int,c(1,0,0,1))    # Two cores
tb5<-block.fit(thuroff.int,c(0,1,1,0))    # Bipartite structure - no cores!

# Examine the results via the sociomatrix:
lab<-tb1$block.membership[tb1$order.vector]
plot.sociomatrix(tb1$blocked.data,labels=list(lab,lab))
lab<-tb2$block.membership[tb2$order.vector]
plot.sociomatrix(tb2$blocked.data,labels=list(lab,lab))
lab<-tb3$block.membership[tb3$order.vector]
plot.sociomatrix(tb3$blocked.data,labels=list(lab,lab))
lab<-tb4$block.membership[tb4$order.vector]
plot.sociomatrix(tb4$blocked.data,labels=list(lab,lab))
lab<-tb5$block.membership[tb5$order.vector]
plot.sociomatrix(tb5$blocked.data,labels=list(lab,lab))

# For more information....
#?blockmodel
#?order
#?plot.sociomatrix
#
#-Eigenvector centrality/coreness-----------------------------------------------
#
# Calculate eigenvector centrality for our two sample cases
ev.kt<-evcent(kaptail.ins)
ev.th<-evcent(thuroff.int)

# How does evcent relate to block membership?  Let's compare:
plot(kaptail.ins,vertex.cex=ev.kt*5+0.5,vertex.col=kb5$block.membership)
plot(thuroff.int,vertex.cex=ev.th*5+0.5,vertex.col=tb5$block.membership)

# Can plot the sociomatrices, sorted by evcent (note the sort order)
plot.sociomatrix(kaptail.ins[order(ev.kt),order(ev.kt)])
plot.sociomatrix(thuroff.int[order(ev.th),order(ev.th)])

# For more information....
# ?evcent