Description Usage Arguments Details Value References Examples
Visualizes the transitions among the cells in the General Cell Mapping approach.
1 | plotCellMapping(feat.object, control)
|
feat.object |
[ |
control |
[ |
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.
[plot].
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 2 3 4 5 6 7 8 9 10 11 12 13 14 | # (1) Define a function:
library(smoof)
f = 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)
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