plot.ecolRxC: Graphical representation of a RxC ecological inference (vote...

In ecolRxC: Ecological Inference of RxC Tables by Latent Structure Approaches

Graphical representation of a RxC ecological inference (vote transfer) matrix

Description

Plot method for objects obtained with ecolRxC.

Usage

## S3 method for class 'ecolRxC'
plot(
  x,
  margins = TRUE,
  digits = 2,
  row.names = NULL,
  col.names = NULL,
  size.numbers = 6,
  size.labels = 4,
  size.margins = 4,
  colour.cells = "cyan4",
  colour.grid = "cornsilk2",
  alpha = 0.5,
  which = NULL,
  ...,
  show.plot = TRUE
)

Arguments

x	An object output of the ecolRxC function.
margins	A TRUE/FALSE argument informing if the margins of the matrix should be displayed. Default TRUE.
digits	Integer indicating the number of decimal places to be shown. Default, 2.
row.names	Names to be used for the rows of the matrix.
col.names	Names to be used for the columns of the matrix.
size.numbers	A reference number indicating the average font size to be used for the transfer numbers. Default, 6.
size.labels	A number indicating the font size to be used for labels. Default, 4.
size.margins	A number indicating the font size to be used for margin numbers. Default, 4.
colour.cells	Background base colour for cells.
colour.grid	Colour to be used for grid lines.
alpha	A [0,1] number of colour transparency.
which	A vector of integers informing the units for which the aggregate transfer matrix should be plotted. Default, NULL, the global matrix is shown.
...	Other arguments passed on to methods. Not currently used.
show.plot	A TRUE/FALSE indicating if the plot should be displayed as a side-effect. By default, TRUE.

Value

Invisibly returns the (ggplot) description of the plot, which is a list with components that contain the plot itself, the data, information about the scales, panels etc.

Note

ggplot2 is needed to be installed for this function to work.

Author(s)

Jose M. Pavia, [e-mail]

Examples

votes1 <- structure(list(P1 = c(16L, 4L, 13L, 6L, 1L, 16L, 6L, 17L, 48L, 14L),
                         P2 = c(8L, 3L, 0L, 5L, 1L, 4L, 7L, 6L, 28L, 8L),
                         P3 = c(38L, 11L, 11L, 3L, 13L, 39L, 14L, 34L, 280L, 84L),
                         P4 = c(66L, 5L, 18L, 39L, 30L, 57L, 35L, 65L, 180L, 78L),
                         P5 = c(14L, 0L, 5L, 2L, 4L, 21L, 6L, 11L, 54L, 9L),
                         P6 = c(8L, 2L, 5L, 3L, 0L, 7L, 7L, 11L, 45L, 17L),
                         P7 = c(7L, 3L, 5L, 2L, 3L, 17L, 7L, 13L, 40L, 8L)),
                         row.names = c(NA, 10L), class = "data.frame")
votes2 <- structure(list(C1 = c(2L, 1L, 2L, 2L, 0L, 4L, 0L, 4L, 19L, 14L),
                         C2 = c(7L, 3L, 1L, 7L, 2L, 5L, 3L, 10L, 21L, 6L),
                         C3 = c(78L, 7L, 28L, 42L, 28L, 84L, 49L, 85L, 260L, 100L),
                         C4 = c(56L, 14L, 20L, 7L, 19L, 54L, 22L, 50L, 330L, 91L),
                         C5 = c(14L, 3L, 6L, 2L, 3L, 14L, 8L, 8L, 45L, 7L)),
                         row.names = c(NA, 10L), class = "data.frame")
example <- ecolRxC(votes1, votes2, method = "IPF")
p <- plot(example, show.plot = FALSE)
p

ecolRxC documentation built on March 31, 2023, 5:50 p.m.