plot.rflexscan {rflexscan}

Graph plotting of flexscan results

Description

Display detected clusters by a graph representation.

Usage

## S3 method for class 'rflexscan'
plot(
  x,
  rank = 1:length(x$cluster),
  pval = 1,
  vertexsize = max(x$input$coordinates[, 1]) - min(x$input$coordinates[, 1]),
  xlab = colnames(x$input$coordinates)[1],
  ylab = colnames(x$input$coordinates)[2],
  xlim = c(min(x$input$coordinates[, 1]), max(x$input$coordinates[, 1])),
  ylim = c(min(x$input$coordinates[, 2]), max(x$input$coordinates[, 2])),
  col = palette(),
  frame_color = "gray40",
  vertex_color = "white",
  ...
)

Arguments

x

An rflexscan object.

rank

An integer vector which specifies ranks of clusters to be displayed.

pval

A threshold of P-value. Clusters with P-values of <pval will be displayed.

vertexsize

Size of vertex of the graph.

xlab

A label of the x axis.

ylab

A label of the y axis.

xlim

The x limits of the plot.

ylim

The y limits of the plot.

col

A vector of colors for each cluster.

frame_color

Color of frames in the graph.

vertex_color

Fill color of vertices that are not included in any clusters.

...

Other parameters to be passed to plot.igraph function.

Details

Clusters are colored using the current palette. Please use palette function to specify colors of each cluster. Note that clusters with ranks larger than the number of colors in the palette are not highlighted.

See Also

rflexscan

Examples

# load sample data (North Carolina SIDS data)
library(spdep)
data("nc.sids")

# calculate the expected numbers of cases
expected <- nc.sids$BIR74 * sum(nc.sids$SID74) / sum(nc.sids$BIR74)

# run FleXScan
fls <- rflexscan(x = nc.sids$x, y = nc.sids$y,
                 observed = nc.sids$SID74,
                 expected = expected,
                 name = rownames(nc.sids),
                 clustersize = 10,
                 nb = ncCR85.nb)

# display all clusters
plot(fls)

# display clusters with rank 1, 2 and 3
plot(fls, rank = c(1, 2, 3))

# display clusters of P-value <= 0.05
plot(fls, pval = 0.05)