seriation (version 1.2-0)

bertinplot: Plot a Bertin Matrix

Description

Plot a data matrix of cases and variables. Each value is represented by a symbol. Large values are highlighted. The matrix can be rearranged to make structure in the data visible (see Falguerolles et al 1997). bertin_cut_line() can be used to add cut lines (see Details).

Usage

bertinplot(x, order = NULL, highlight = TRUE,  options = NULL)

Arguments

x

a data matrix. Note that following Bertin, columns are variables and rows are cases. This behavior can be reversed using reverse = TRUE in options.

order

an object of class ser_permutation to rearrange x before plotting. If NULL, no rearrangement is performed.

highlight

a logical scalar indicating whether to use highlighting. If TRUE, all variables with values greater than the variable-wise mean are highlighted. To control highlighting, also a logical matrix with the same dimensions as

options

a list with options for plotting. The list can contain the following elements:

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[ob

Details

The plot is organized as a matrix of symbols. The symbols are drawn by a panel function, where all symbols of a row are drawn by one call of the function (using vectorization). The interface for the panel function is panel.myfunction(value, spacing, hl). value is the vector of values for a row scaled between 0 and 1, spacing contains the relative space between symbols and hl is a logical vector indicating which symbol should be highlighted.

Cut lines can be added to an existing bertin plot using bertin_cut_line(x=NULL, y=NULL). x/y is can be a number indicating where to draw the cut line between two columns/rows. If both x and y is specified then one can select a row/column and the other can select a range to draw a line which does only span a part of the row/column. It is important to call bertinplot() with the option pop=FALSE.

References

de Falguerolles, A., Friedrich, F., Sawitzki, G. (1997): A Tribute to J. Bertin's Graphical Data Analysis. In: Proceedings of the SoftStat '97 (Advances in Statistical Software 6), 11--20.

See Also

ser_permutation, seriate, Package grid.

Examples

data("Irish")
scale_by_rank <- function(x) apply(x, 2, rank)
x <- scale_by_rank(Irish[,-6])

## use the the sum of absolute rank differences
order <- c(
  seriate(dist(x, "minkowski", p = 1)),
  seriate(dist(t(x), "minkowski", p = 1))
)

## plot
bertinplot(x, order)

## some alternative displays
bertinplot(x, order, options = list(shading = TRUE, panel = panel.blocks))
bertinplot(x, order, options = list(panel = panel.lines))
bertinplot(x, order, options = list(panel = panel.squares))
bertinplot(x, order, 
    options = list(panel = panel.circles, spacing = -0.5))

## plot with cut lines (we manually set the order here)
order <- ser_permutation(c(21, 16, 19, 18, 14, 12, 20, 15, 
    17, 26, 13, 41,  7, 11, 5, 23, 28, 34, 31, 1, 38, 40,  
    3, 39,  4, 27, 24,  8, 37, 36, 25, 30, 33, 35,  2, 
    22, 32, 29, 10,  6,  9),
    c(4, 2, 1, 6, 8, 7, 5, 3))

bertinplot(x, order, options=list(pop=FALSE))
bertin_cut_line(,4) ## horizontal line between rows 4 and 5
bertin_cut_line(,7) ## separate "Right to Life" from the rest
bertin_cut_line(14,c(0,4)) ## separate a block of large values (vertically)