plotCellMapping {flacco} | R Documentation |
Plot Cell Mapping
Description
Visualizes the transitions among the cells in the General Cell Mapping approach.
Usage
plotCellMapping(feat.object, control)
Arguments
feat.object |
[ |
control |
[ |
Details
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 is256
.
Plot Control:
gcm.margin
: The margins of the plot as used bypar("mar")
. The default isc(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 fromggplot2
.gcm.plot_arrows
: Should arrows be plotted? The default isTRUE
.gcm.arrow.length_{x, y}
: Scaling factor of the arrow length in x- and y-direction. The default is0.9
, 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 isTRUE
.
Value
[plot
].
References
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).
Examples
# (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)