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 = getOption("mc.cores", 2L), ...)

## Arguments

vismodeldata |
(required) quantum catch color 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 vector containing indicating the group to wich 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) |

cores |
number of cores to be used in parallel processing. If `1` , parallel
computing will not be used. Defaults to `getOption("mc.cores", 2L)` |

... |
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 `achro = TRUE`

).

## Examples

# NOT RUN {
data(sicalis)
vm <- vismodel(sicalis, achro='bt.dc')
gr <- gsub("ind..", "", rownames(vm))
bootcoldist(vm, gr, n=c(1,2,2,4), weber=0.1, weber.achro=0.1, cores=1)
# }