Description Usage Arguments Details References See Also Examples

Transformation of R colors by simulating color vision deficiencies, based on a CVD transform matrix.

1 2 3 4 5 6 7 8 9 | ```
simulate_cvd(col, cvd_transform)
deutan(col, severity = 1)
protan(col, severity = 1)
tritan(col, severity = 1)
interpolate_cvd_transform(cvd, severity = 1)
``` |

`col` |
character. A color or vector of colors, e.g., |

`cvd_transform` |
numeric 3x3 matrix, specifying the color vision deficiency transform matrix. |

`severity` |
numeric. Severity of the color vision defect, a number between 0 and 1. |

`cvd` |
list of cvd transformation matrices. See |

Using the physiologically-based model for simulating color vision deficiency (CVD)
of Machado et al. (2009), different kinds of limitations can be
emulated: deuteranope (green cone cells defective), protanope (red cone cells defective),
and tritanope (blue cone cells defective).
The workhorse function to do so is `simulate_cvd`

which can take any vector
of valid R colors and transform them according to a certain CVD transformation
matrix (see `cvd`

) and transformation equation.

The functions `deutan`

, `protan`

, and `tritan`

are the high-level functions for
simulating the corresponding kind of colorblindness with a given severity.
Internally, they all call `simulate_cvd`

along with a (possibly interpolated)
version of the matrices from `cvd`

. Matrix interpolation can be carried out with
the function `interpolate_cvd_transform`

(see Examples).

If input `col`

is a matrix with three rows named `R`

, `G`

, and
`B`

(top down) they are interpreted as Red-Green-Blue values within the
range `[0-255]`

. Instead of an (s)RGB color vector a matrix of the same size as the
input `col`

with the corresponding simulated Red-Green-Blue values will be returned.
This can be handy to avoid too many conversions.

Machado GM, Oliveira MM, Fernandes LAF (2009).
A Physiologically-Based Model for Simulation of Color Vision Deficiency.
*IEEE Transactions on Visualization and Computer Graphics*. **15**(6), 1291–1298.
doi: 10.1109/TVCG.2009.113
Online version with supplements at
http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html.

Zeileis A, Fisher JC, Hornik K, Ihaka R, McWhite CD, Murrell P, Stauffer R, Wilke CO (2019). “ccolorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.” arXiv:1903.06490, arXiv.org E-Print Archive. http://arxiv.org/abs/1903.06490

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# simulate color-vision deficiency by calling `simulate_cvd` with specified matrix
simulate_cvd(c("#005000", "blue", "#00BB00"), tritanomaly_cvd["6"][[1]])
# simulate color-vision deficiency by calling the shortcut high-level function
tritan(c("#005000", "blue", "#00BB00"), severity = 0.6)
# simulate color-vision deficiency by calling `simulate_cvd` with interpolated cvd matrix
simulate_cvd(c("#005000", "blue", "#00BB00"),
interpolate_cvd_transform(tritanomaly_cvd, severity = 0.6))
# apply CVD directly on RGB matrix
RGB <- t(hex2RGB(rainbow(3))@coords*255)
deutan(RGB)
``` |

colorspace documentation built on May 2, 2019, 12:49 p.m.

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