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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