R: Simulate Color Vision Deficiency

simulate_cvd {colorspace} | R Documentation

## Simulate Color Vision Deficiency

### Description

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

### Usage

    simulate_cvd(col, cvd_transform)

    deutan(col, severity = 1)

    protan(col, severity = 1)

    tritan(col, severity = 1)

    interpolate_cvd_transform(cvd, severity = 1)

### Arguments

`col` | character. A color or vector of colors, e.g., `"#FFA801"` or `"blue"`. Input `col` can also be a matrix with three rows containing R/G/B (0-255) values, see details.
`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 `cvd` for available options.

### Details

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.

### References

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

### See Also

`cvd`

### Examples

    # 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)

[Package colorspace version 2.0-0 Index]