Uses a bootstrap procedure to generate confidence intervals for the mean colour distance between two or more samples of colours

bootcoldist(vismodeldata, by, boot.n = 1000, alpha = 0.95, raw = FALSE, ...)

Arguments

vismodeldata

(required) quantum catch colour data. Can be the result from vismodel(), or colspace(). Data may also be independently calculated quantum catches, in the form of a data frame with columns representing photoreceptors.

by

(required) a numeric or character vector indicating the group to which each row from the object belongs to.

boot.n

number of bootstrap replicates (defaults to 1000)

alpha

the confidence level for the confidence intervals (defaults to 0.95)

raw

should the full set of bootstrapped distances (equal in length to boot.n) be returned, instead of the summary distances and CI's? Defaults to FALSE.

...

other arguments to be passed to coldist(). Must at minimum include n and weber. See coldist() for details.

Value

a matrix including the empirical mean and bootstrapped confidence limits for dS (and dL if achromatic = TRUE), or a data.frame of raw bootstraped dS (and dL if achromatic = TRUE) values equal in length to boot.n.

Details

You can customise the type of parallel processing used by this function with the future::plan() function. This works on all operating systems, as well as high performance computing (HPC) environment. Similarly, you can customise the way progress is shown with the progressr::handlers() functions (progress bar, acoustic feedback, nothing, etc.)

References

Maia, R., White, T. E., (2018) Comparing colors using visual models. Behavioral Ecology, ary017 doi: 10.1093/beheco/ary017

Examples

# \donttest{ # Run the receptor-noise limited model, using the visual phenotype # of the blue tit data(sicalis) vm <- vismodel(sicalis, achromatic = "bt.dc", relative = FALSE) gr <- gsub("ind..", "", rownames(vm)) bootcoldist(vm, by = gr, n = c(1, 2, 2, 4), weber = 0.1, weber.achro = 0.1)
#> Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
#> dS.mean dS.lwr dS.upr dL.mean dL.lwr dL.upr #> B-C 4.626548 3.0446919 6.861227 7.4677171 5.6748829 9.282542 #> B-T 1.741764 0.2188554 5.014370 0.2340913 0.0193291 1.616231 #> C-T 6.110519 4.1550209 8.637632 7.2336258 5.4127958 9.045694
# Run the same again, though as a simple colourspace model data(sicalis) vm <- vismodel(sicalis, achromatic = "bt.dc") space <- colspace(vm) gr <- gsub("ind..", "", rownames(space)) bootcoldist(space, by = gr)
#> Quantum catch are relative, distances may not be meaningful
#> Calculating unweighted Euclidean distances and Weber luminance contrast
#> dS.mean dS.lwr dS.upr dL.mean dL.lwr dL.upr #> B-C 0.08873222 0.057492091 0.12509677 1.11017675 0.770862609 1.5208958 #> B-T 0.02510854 0.005678686 0.07323532 0.02368528 0.002474378 0.1646752 #> C-T 0.11208967 0.080721573 0.14476401 1.06135304 0.722669275 1.4676965
# Estimate bootstrapped colour-distances for a more 'specialised' model, # like the colour hexagon data(flowers) vis.flowers <- vismodel(flowers, visual = "apis", qcatch = "Ei", relative = FALSE, vonkries = TRUE, achromatic = "l", bkg = "green" ) flowers.hex <- colspace(vis.flowers, space = "hexagon") pop_group <- c(rep("pop_1", nrow(flowers.hex) / 2), rep("pop_2", nrow(flowers.hex) / 2)) bootcoldist(flowers.hex, by = pop_group)
#> Calculating unweighted Euclidean distances and simple luminance contrast
#> dS.mean dS.lwr dS.upr dL.mean dL.lwr dL.upr #> pop_1-pop_2 0.0736203 0.02299502 0.2012074 0.9016919 0.7288422 1.057964
# }