plot.critFun {blockmodeling} R Documentation

## Functions for plotting a partitioned matrix (representing the network)

### Description

The main function plot.mat or plotMat plots a (optionally partitioned) matrix. If the matrix is partitioned, the rows and columns of the matrix are rearranged according to the partitions. Other functions are only wrappers for plot.mat or plotMat for convenience when plotting the results of the corresponding functions. The plotMatNm plots two matrices based on M, normalized by rows and columns, next to each other. The plotArray plots an array. plot.mat.nm has been replaced by plotMatNm.

### Usage

## S3 method for class 'critFun'
plot(x, main = NULL, ...)

## S3 method for class 'crit.fun'
plot(x, main = NULL, ...)

plotMatNm(
M = x,
x = M,
...,
main.title = NULL,
title.row = "Row normalized",
title.col = "Column normalized",
main.title.line = -2,
par.set = list(mfrow = c(1, 2))
)

## S3 method for class 'optMorePar'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'opt.more.par'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'optMoreParMode'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'opt.more.par.mode'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'optPar'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'opt.par'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'optParMode'
plot(x, main = NULL, which = 1, ...)

## S3 method for class 'opt.par.mode'
plot(x, main = NULL, which = 1, ...)

plotMat(
x = M,
clu = NULL,
orderClu = FALSE,
M = x,
ylab = "",
xlab = "",
main = NULL,
print.val = !length(table(M)) <= 2,
print.0 = FALSE,
plot.legend = !print.val && !length(table(M)) <= 2,
print.legend.val = "out",
print.digits.legend = 2,
print.digits.cells = 2,
print.cells.mf = NULL,
outer.title = FALSE,
title.line = ifelse(outer.title, -1.5, 7),
mar = c(0.5, 7, 8.5, 0) + 0.1,
cex.val = "default",
val.y.coor.cor = 0,
val.x.coor.cor = 0,
cex.legend = 1,
legend.title = "Legend",
cex.axes = "default",
print.axes.val = NULL,
print.x.axis.val = !is.null(colnames(M)),
print.y.axis.val = !is.null(rownames(M)),
x.axis.val.pos = 1.01,
y.axis.val.pos = -0.01,
cex.main = par()$cex.main, cex.lab = par()$cex.lab,
yaxis.line = -1.5,
xaxis.line = -1,
legend.left = 0.4,
legend.up = 0.03,
legend.size = 1/min(dim(M)),
legend.text.hor.pos = 0.5,
par.line.width = 3,
par.line.col = "blue",
IM.dens = NULL,
IM = NULL,
wnet = NULL,
wIM = NULL,
use.IM = length(dim(IM)) == length(dim(M)) | !is.null(wIM),
dens.leg = c(null = 100, nul = 100),
blackdens = 70,
plotLines = FALSE,
frameMatrix = TRUE,
x0ParLine = -0.1,
x1ParLine = 1,
y0ParLine = 0,
y1ParLine = 1.1,
colByUnits = NULL,
colByRow = NULL,
colByCol = NULL,
mulCol = 2,
joinColOperator = "+",
colTies = FALSE,
maxValPlot = NULL,
printMultipliedMessage = TRUE,
colLabels = FALSE,
...
)

plotArray(
x = M,
M = x,
IM = NULL,
...,
main.title = NULL,
main.title.line = -2,
mfrow = NULL
)

## S3 method for class 'mat'
plot(
x = M,
clu = NULL,
orderClu = FALSE,
M = x,
ylab = "",
xlab = "",
main = NULL,
print.val = !length(table(M)) <= 2,
print.0 = FALSE,
plot.legend = !print.val && !length(table(M)) <= 2,
print.legend.val = "out",
print.digits.legend = 2,
print.digits.cells = 2,
print.cells.mf = NULL,
outer.title = FALSE,
title.line = ifelse(outer.title, -1.5, 7),
mar = c(0.5, 7, 8.5, 0) + 0.1,
cex.val = "default",
val.y.coor.cor = 0,
val.x.coor.cor = 0,
cex.legend = 1,
legend.title = "Legend",
cex.axes = "default",
print.axes.val = NULL,
print.x.axis.val = !is.null(colnames(M)),
print.y.axis.val = !is.null(rownames(M)),
x.axis.val.pos = 1.01,
y.axis.val.pos = -0.01,
cex.main = par()$cex.main, cex.lab = par()$cex.lab,
yaxis.line = -1.5,
xaxis.line = -1,
legend.left = 0.4,
legend.up = 0.03,
legend.size = 1/min(dim(M)),
legend.text.hor.pos = 0.5,
par.line.width = 3,
par.line.col = "blue",
IM.dens = NULL,
IM = NULL,
wnet = NULL,
wIM = NULL,
use.IM = length(dim(IM)) == length(dim(M)) | !is.null(wIM),
dens.leg = c(null = 100, nul = 100),
blackdens = 70,
plotLines = FALSE,
frameMatrix = TRUE,
x0ParLine = -0.1,
x1ParLine = 1,
y0ParLine = 0,
y1ParLine = 1.1,
colByUnits = NULL,
colByRow = NULL,
colByCol = NULL,
mulCol = 2,
joinColOperator = "+",
colTies = FALSE,
maxValPlot = NULL,
printMultipliedMessage = TRUE,
colLabels = FALSE,
...
)


### Arguments

 x A result from a corresponding function or a matrix or similar object representing a network. main Main title. ... Aditional arguments to plot.default for plotMat and also to plotMat for other functions. M A matrix or similar object representing a network - either x or M must be supplied - both are here to make the code compatible with generic and with older functions. main.title Main title in plotArray version. title.row Title for the row-normalized matrix in nm version title.col Title for the column-normalized matrix in nm version main.title.line The line in which main title is printed in plotArray version. par.set A list of possible plotting parameters (to par) to be used in nm version which Which (if there are more than one) of optimal solutions to plot. clu A partition. Each unique value represents one cluster. If the network is one-mode, then this should be a vector, else a list of vectors, one for each mode. orderClu Should the partition be ordered before plotting. FALSE by default. If TRUE, orderClu is used (using default arguments) to order the clusters in a partition in "decearsing" (see orderClu for interpretation) order. ylab Label for y axis. xlab Label for x axis. print.val Should the values be printed in the matrix. print.0 If print.val = TRUE Should the 0s be printed in the matrix. plot.legend Should the legend for shades be plotted. print.legend.val Should the values be printed in the legend. print.digits.legend The number of digits that should appear in the legend. print.digits.cells The number of digits that should appear in the cells (of the matrix and/or legend). print.cells.mf If not NULL, the above argument is ignored, the cell values are printed as the cell are multiplied by this factor and rounded. outer.title Should the title be printed on the 'inner' or 'outer' margin of the plot, default is 'inner' margin. title.line The line (from the top) where the title should be printed. The suitable values depend heavily on the displayed type. mar A numerical vector of the form c(bottom, left, top, right) which gives the lines of margin to be specified on the four sides of the plot. The R default for ordinary plots is c(5, 4, 4, 2) + 0.1, while this function default is c(0.5, 7, 8.5, 0) + 0.1. cex.val The size of the values printed. The default is 10 / 'number of units'. val.y.coor.cor Correction for centering the values in the squares in y direction. val.x.coor.cor Correction for centering the values in the squares in x direction. cex.legend Size of the text in the legend. legend.title The title of the legend. cex.axes Size of the characters in axes. Default makes the cex so small that all categories can be printed. print.axes.val Should the axes values be printed. Default prints each axis if rownames or colnames is not NULL. print.x.axis.val Should the x axis values be printed. Default prints each axis if rownames or colnames is not NULL. print.y.axis.val Should the y axis values be printed. Default prints each axis if rownames or colnames is not NULL. x.axis.val.pos The x coordinate of the y axis values. y.axis.val.pos The y coordinate of the x axis values. cex.main Size of the text in the main title. cex.lab Size of the text in matrix. yaxis.line The position of the y axis (the argument 'line'). xaxis.line The position of the x axis (the argument 'line'). legend.left How much left should the legend be from the matrix. legend.up How much up should the legend be from the matrix. legend.size Relative legend size. legend.text.hor.pos Horizontal position of the legend text (bottom) - 0 = bottom, 0.5 = middle,... par.line.width The width of the line that separates the partitions. par.line.col The color of the line that separates the partitions. IM.dens The density of shading lines in each block. IM The image (as obtained with critFunC) of the blockmodel. dens.leg is used to translate this image into IM.dens. wnet Specifies which matrix (if more) should be plotted - used if M is an array. wIM Specifies which IM (if more) should be used for plotting. The default value is set to wnet) - used if IM is an array. use.IM Specifies if IM should be used for plotting. dens.leg It is used to translate the IM into IM.dens. blackdens At which density should the values on dark colors of lines be printed in white. plotLines Should the lines in the matrix be printed. The default value is set to FALSE, best set to TRUE for very small networks. frameMatrix Should the matrix be framed (if plotLines is FALSE). The default value is set to TRUE. x0ParLine Coordinates for lines separating clusters. x1ParLine Coordinates for lines separating clusters. y0ParLine Coordinates for lines separating clusters. y1ParLine Coordinates for lines separating clusters. colByUnits Coloring units. It should be a vector of unit length. colByRow Coloring units by rows. It should be a vector of unit length. colByCol Coloring units by columns. It should be a vector of unit length. mulCol Multiply color when joining with row, column. Only used when when colByUnits is not NULL. joinColOperator Function to join colByRow and colByCol. The default value is set to "+". colTies If TRUE, ties are colored, if FALSE, 0-ties are colored. maxValPlot The value to use as a maximum when computing colors (ties with maximal positive value are plotted as black). printMultipliedMessage Should the message '* all values in cells were multiplied by' be printed on the plot. The default value is set to TRUE. replaceNAdiagWith0 If replaceNAdiagWith0 = TRUE Should the NA values on the diagonal of a matrix be replaced with 0s. colLabels Should the labels of units be colored. If FALSE, these are not collored, if TRUE, they are colored with colors of clusters as defined by palette. This can be aslo a vector of colors (or integers) for one-mode networks or a list of two such vectors for two-mode networks. mfrow mfrow Argument to par - number of row and column plots to be plotted on one figure.

### Value

The functions are used for their side effect - plotting.

Aleš Žiberna

### References

Žiberna, A. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002

Žiberna, A. (2008). Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. Journal of Mathematical Sociology, 32(1), 57-84. doi: 10.1080/00222500701790207

critFunC, optRandomParC

### Examples

# Generation of the network
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] <- 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)

# Ploting the network
plotMat(M = net, clu = clu, print.digits.cells = 3)
class(net) <- "mat"
plot(net, clu = clu)
# See corresponding functions for examples for other ploting
# functions
# presented, that are essentially only the wrappers for "plot.max"


[Package blockmodeling version 1.0.5 Index]