`simulate_cvd.Rd`

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

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 (2020).
“colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.”
*Journal of Statistical Software*, **96**(1), 1--49. doi: 10.18637/jss.v096.i01

# simulate color-vision deficiency by calling `simulate_cvd` with specified matrix simulate_cvd(c("#005000", "blue", "#00BB00"), tritanomaly_cvd["6"][[1]])#> [1] "#004D19" "#000FAD" "#00B53B"# simulate color-vision deficiency by calling the shortcut high-level function tritan(c("#005000", "blue", "#00BB00"), severity = 0.6)#> [1] "#004D19" "#000FAD" "#00B53B"# 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))#> [1] "#004D19" "#000FAD" "#00B53B"#> [,1] [,2] [,3] #> R 94 219 0 #> G 71 171 12 #> B 0 11 247