Visualizes the transitions among the cells in the General Cell Mapping approach.
plotCellMapping(feat.object, control)
feat.object | [ |
---|---|
control | [ |
[plot
].
Possible control
arguments are:
Computation of GCM Features:
gcm.approach
: Which approach should be used when
computing the representatives of a cell. The default is "min"
,
i.e. the observation with the best (minimum) value within per cell.
gcm.cf_power
: Theoretically, we need to compute the
canonical form to the power of infinity. However, we use this value
as approximation of infinity. The default is 256
.
Plot Control:
gcm.margin
: The margins of the plot as used by
par("mar")
. The default is c(5, 5, 4, 4)
.
gcm.color_attractor
: Color of the attractors. The
default is "#333333"
, i.e. dark grey.
gcm.color_uncertain
: Color of the uncertain cells. The
default is "#cccccc"
, i.e. grey.
gcm.color_basin
: Color of the basins of attraction. This
has to be a function, which computes the colors, depending on the
number of attractors. The default is the color scheme from ggplot2
.
gcm.plot_arrows
: Should arrows be plotted? The default
is TRUE
.
gcm.arrow.length_{x, y}
: Scaling factor of the arrow
length in x- and y-direction. The default is 0.9
, i.e. 90%
of the actual length.
gcm.arrowhead.{length, width}
: Scaling factor for the
width and length of the arrowhead. Per default (0.1
) the
arrowhead is 10% of the length of the original arrow.
gcm.arrowhead.type
: Type of the arrowhead. Possible
options are "simple"
, "curved"
, "triangle"
(default), "circle"
, "ellipse"
and "T"
.
gcm.color_grid
: Color of the grid lines. The default is
"#333333"
, i.e. dark grey.
gcm.label.{x, y}_coord
: Label of the x-/y-coordinate
(below / left side of the plot).
gcm.label.{x, y}_id
: Label of the x-/y-cell ID (above /
right side of the plot).
gcm.plot_{coord, id}_labels
: Should the coordinate
(bottom and left) / ID (top and right) labels be plotted? The default
is TRUE
.
Kerschke, P., Preuss, M., Hernandez, C., Schuetze, O., Sun, J.-Q., Grimme, C., Rudolph, G., Bischl, B., and Trautmann, H. (2014): “Cell Mapping Techniques for Exploratory Landscape Analysis”, in: EVOLVE -- A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, pp. 115-131 (http://dx.doi.org/10.1007/978-3-319-07494-8_9).
# (1) Define a function: library(smoof)#>#>#>#> Warning: package ‘checkmate’ was built under R version 3.4.1f = makeHosakiFunction() # (2) Create a feature object: X = cbind( x1 = runif(n = 100, min = -32, max = 32), x2 = runif(n = 100, min = 0, max = 10) ) y = apply(X, 1, f) feat.object = createFeatureObject(X = X, y = y, blocks = c(4, 6)) # (3) Plot the cell mapping: plotCellMapping(feat.object)