hasse: Visualization of Hasse diagram specified by an adjacency...
In paircompviz: Multiple comparison test visualization

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/hasse.R

Given an adjacency matrix, this function displays the corresponding Hasse diagram. This is a wrapper function for graph creation using the Rgraphviz package.

hasse(e,
      v=NULL,
      elab="",
      ecol="black",
      ebg="gray",
      vcol="black",
      vbg="white",
      vsize=1,
      fvlab=".",
      fvcol="black",
      fvbg="white",
      fvsize=1,
      febg="black",
      fesize=1,
      main=paste("Hasse Diagram of", deparse(substitute(e))),
      compress=FALSE)

`e`	An adjacency matrix, with e_{i,j} indicating the edge size between vertices i and j (e_{i,j} = 0 means no edge between i and j). The matrix must be rectangular with non-negative non-missing values.
`v`	Vector of names of the vertices. If null, the vertex names will be obtained from column names of adjacency matrix e.
`elab`	Labels of the edges. If it is a scalar value, all edges would have the same label. Otherwise, elab must be a rectangular matrix (similar to adjacency matrix e). A value on i-th row and j-th column is a label of the edge between vertex i and vertex j.
`ecol`	Edge label color. If scalar, all edge labels have the same color. Otherwise, ecol must be in the form of adjacency matrix: a value on i-th row and j-th column is a color of the label of the edge between vertex i and vertex j.
`ebg`	Edge line color. If scalar, all edges have the same color. Otherwise, ebg must be in the form of adjacency matrix: a value on i-th row and j-th column is a color of the edge between vertex i and vertex j.
`vcol`	Vertex label color. If scalar, all vertices have the same label color. Otherwise, vcol must be a vector of the size corresponding to the number of vertices.
`vbg`	Vertex background color. If scalar, all vertices have the same background color. Otherwise, vcol must be a vector of the size corresponding to the number of vertices.
`vsize`	Vertex sizes. If scalar, all vertices have the same size in the image. Otherwise, vsize must be a vector of the size corresponding to the number of vertices.
`fvlab`	Labels of "dot" vertices. Must be scalar.
`fvcol`	"dot" vertex label color. Must be scalar.
`fvbg`	"dot" vertex background color. Must be scalar.
`fvsize`	"dot" vertex size. Must be scalar.
`febg`	Color of edges introduced by edge compression. Must be scalar.
`fesize`	Thickness of edges introduced by edge compression. Must be scalar and non-negative.
`main`	Main title of the diagram.
`compress`	`TRUE` if the edges should be compressed, i.e. if the maximum bi-cliques have to be found in the graph and replaced with a "dot" vertex. (See examples.)

This function depicts a Hasse diagram specified with an adjacency matrix e. Hasse diagram is a visualization of partially ordered set, by drawing its transitive reduction as an oriented graph. Each vertex corresponds to an element of the set. There is an edge between vertex i and vertex j iff i < j and there is no z such that i < z < j.

The function is also capable of edge compression via introducing the "dot" edges: Let U, V be two disjoint non-empty sets of edges, such that for each u from U and v from V, there exists an edge from u to v. (The number of such edges equals |U| \cdot |V|.) Starting from |U| > 2 and |V| > 2, the Hasse diagram may become too complicated and hence confusing. Therefore a compress argument exists in this function that enables “compression” of the edges in such a way that a new “dot” node w is introduced and |U| \cdot |V| edges between sets U and V are replaced with |U|+|V| edges from set U to node w and from node w to set V.

Nothing.

Michal Burda

paircomp

  # linear order
  e <- matrix(c(0, 1, 1, 0, 0, 1, 0, 0, 0), nrow=3, byrow=TRUE)
  hasse(e)

  # prepare adjacency matrix
  m <- matrix(0, byrow=TRUE, nrow=5, ncol=5)
  m[3, 1] <- 1
  m[3, 2] <- 1
  m[4, 1] <- 9
  m[4, 2] <- 1
  m[5, 1] <- 1
  m[5, 2] <- 1
  m

  mc <- m
  mc[mc > 0] <- "red"
  ms <- m
  ms[ms > 0] <- "blue"

  # view m with default settings
  hasse(m, ebg="black")

  # view m WITHOUT edge compression and some fancy adjustments
  hasse(v=c("a", "b", "c", "d", "e"), 
             vcol=c(gray(0.5), gray(1), rep(gray(0), 3)), 
             vbg=gray(5:1/5), vsize=1:5, e=m, ecol=mc, ebg=ms, elab=m,
             compress=FALSE)

  # view m WITH edge compression and some fancy adjustments
  hasse(v=c("a", "b", "c", "d", "e"), 
             vcol=c(gray(0.5), gray(1), rep(gray(0), 3)), 
             vbg=gray(5:1/5), vsize=1:5, e=m, ecol=mc, ebg=ms, elab=m,
             compress=TRUE)

Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Loading required package: grid
     [,1] [,2] [,3] [,4] [,5]
[1,]    0    0    0    0    0
[2,]    0    0    0    0    0
[3,]    1    1    0    0    0
[4,]    9    1    0    0    0
[5,]    1    1    0    0    0