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, cores = NULL, ...)
(required) quantum catch colour data.
Can be the result from
(required) a numeric or character vector indicating the group to which each row from the object belongs to.
number of bootstrap replicates (defaults to 1000)
the confidence level for the confidence intervals (defaults to 0.95)
other arguments to be passed to
a matrix including the empirical mean and bootstrapped
confidence limits for dS (and dL if
achromatic = TRUE).
You can customise the type of parallel processing used by this function with
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
(progress bar, acoustic feedback, nothing, etc.)
Maia, R., White, T. E., (2018) Comparing colors using visual models. Behavioral Ecology, ary017 doi: 10.1093/beheco/ary017