network_plot: Plot graph

In memnet: Network Tools for Memory Research

Description

Custom graph plot using igraph's layout functions.

Usage

network_plot(adj, names = NULL,
  layout = igraph::layout.fruchterman.reingold, nod_col = "#E0EED4",
  nod_cex = 3, nod_shadow = T, edg_col = "grey25", edg_lwd = 1.5,
  lab_col = "black", lab_cex = 1, lab_lwd = 1, lab_lcol = "grey25",
  lab_grid_size = 48, lab_padding = c(3, 3))

Arguments

adj	numeric matrix representing the adjacency matrix. Can also be an object of class "igraph" or an edge list, i.e., a two-column matrix or data.frame containing specifying the edges start and end points.
names	optional character vector specifying the node names. Must be of appropriate length.
layout	layout function from the igraph package. Default is layout.fruchterman.reingold.
nod_col	character vector of length 1 or length |V| specifying the node colors.
nod_cex	numeric speciying the size of the node circles.
nod_shadow	logical specifiying whether nodes should have shadows. Node shodow color is created from darkening node_col.
edg_col	character vector of length 1 of length |E| specifying the edge line colors.
edg_lwd	numeric vector of length 1 of length |E| specifying the edge line widths.
lab_col	character vector of length 1 of length |V| specifying the text label colors.
lab_cex	numeric vector of length 1 of length |V| specifying the text label sizes.
lab_lwd	numeric vector of length 1 of length |V| specifying the width of the lines connecting the text label to the nodes.
lab_lcol	character vector of length 1 of length |E| specifying specifying the color of the lines connecting the text label to the nodes.
lab_grid_size	integer specifying the grid size used to place the node labels. Canvas is split in lab_grid_size and labels are placed into cells closest to the associated node.
lab_padding	numeric vector of length 2 specifying the spacing among labels and between labels and nodes on the x and y dimension.

Value

nothing. A plot is created in dev.cur.

Examples

## Not run: 
# get fluency data
data(animal_fluency)

# edge list of fluency graph
edge_list = threshold_graph(animal_fluency[1:20])

# get adjacency matrix
adj = edg_to_adj(edge_list)

# plot
network_plot(adj)

## End(Not run)

memnet documentation built on May 2, 2019, 9:35 a.m.