Returns the attributes of colspace objects.

# S3 method for colspace
summary(object, by = NULL, ...)

Arguments

object

(required) a colspace object.

by

when the input is in tcs colourspace, by is either a single value specifying the range of colour points for which summary tetrahedral-colourspace variables should be calculated (for example, by = 3 indicates summary will be calculated for groups of 3 consecutive colour points (rows) in the quantum catch colour data frame) or a vector containing identifications for the rows in the quantum catch colour data frame (in which case summaries will be calculated for each group of points sharing the same identification). If by is left blank, the summary statistics are calculated across all colour points in the data.

...

class consistency (ignored).

Value

returns all attributes of the data as mapped to the selected colourspace, including options specified when calculating the visual model. Also return the default data.frame summary, except when the object is the result of tcspace(), in which case the following variables are output instead:

  • centroid.u, .s, .m, .l the centroids of usml coordinates of points.

  • c.vol the total volume occupied by the points, computed with a convex hull.

  • rel.c.vol volume occupied by the points (convex hull volume) relative to the tetrahedron volume.

  • colspan.m the mean hue span.

  • colspan.v the variance in hue span.

  • huedisp.m the mean hue disparity.

  • huedisp.v the variance in hue disparity.

  • mean.ra mean saturation.

  • max.ra maximum saturation achieved by the group of points.

  • a.vol colour volume computed with α-shapes.

References

Stoddard, M. C., & Prum, R. O. (2008). Evolution of avian plumage color in a tetrahedral color space: A phylogenetic analysis of new world buntings. The American Naturalist, 171(6), 755-776.

Endler, J. A., & Mielke, P. (2005). Comparing entire colour patterns as birds see them. Biological Journal Of The Linnean Society, 86(4), 405-431.

Gruson H. 2020. Estimation of colour volumes as concave hypervolumes using α-shapes. Methods in Ecology and Evolution, early view doi: 10.1111/2041-210X.13398

Author

Rafael Maia rm72@zips.uakron.edu

Examples

# Colour hexagon
data(flowers)
vis.flowers <- vismodel(flowers,
  visual = "apis", qcatch = "Ei", relative = FALSE,
  vonkries = TRUE, bkg = "green"
)
flowers.hex <- hexagon(vis.flowers)
summary(flowers.hex)
#> Colorspace & visual model options:
#>  * Colorspace: hexagon 
#>  * Quantal catch: Ei 
#>  * Visual system, chromatic: apis 
#>  * Visual system, achromatic: none 
#>  * Illuminant: ideal, scale = 1 (von Kries colour correction applied) 
#>  * Background: green 
#>  * Relative: FALSE 
#>  * Max possible chromatic volume: NA 
#> 
#>        s                 m                l                x           
#>  Min.   :0.01889   Min.   :0.1623   Min.   :0.1863   Min.   :-0.27348  
#>  1st Qu.:0.33717   1st Qu.:0.7202   1st Qu.:0.6943   1st Qu.: 0.09593  
#>  Median :0.47192   Median :0.7827   Median :0.8103   Median : 0.27697  
#>  Mean   :0.46048   Mean   :0.7190   Mean   :0.7559   Mean   : 0.25584  
#>  3rd Qu.:0.57925   3rd Qu.:0.8456   3rd Qu.:0.8484   3rd Qu.: 0.40041  
#>  Max.   :0.90735   Max.   :0.9112   Max.   :0.9001   Max.   : 0.66006  
#>        y               h.theta            r.vec            sec.fine    
#>  Min.   :-0.23770   Min.   :  1.103   Min.   :0.09195   Min.   :  0.0  
#>  1st Qu.: 0.06693   1st Qu.: 52.549   1st Qu.:0.23098   1st Qu.: 50.0  
#>  Median : 0.15522   Median : 61.741   Median :0.32881   Median : 60.0  
#>  Mean   : 0.11077   Mean   : 77.206   Mean   :0.34600   Mean   : 72.5  
#>  3rd Qu.: 0.20693   3rd Qu.: 77.511   3rd Qu.:0.45400   3rd Qu.: 72.5  
#>  Max.   : 0.30778   Max.   :271.076   Max.   :0.70155   Max.   :270.0  
#>   sec.coarse       
#>  Length:36         
#>  Class :character  
#>  Mode  :character  
#>                    
#>                    
#>                    

# Tetrahedral model
data(sicalis)
vis.sicalis <- vismodel(sicalis, visual = "avg.uv")
csp.sicalis <- colspace(vis.sicalis)
summary(csp.sicalis, by = rep(c("C", "T", "B"), 7))
#> Colorspace & visual model options:
#>  * Colorspace: tcs 
#>  * Quantal catch: Qi 
#>  * Visual system, chromatic: avg.uv 
#>  * Visual system, achromatic: none 
#>  * Illuminant: ideal, scale = 1 (von Kries colour correction not applied) 
#>  * Background: ideal 
#>  * Relative: TRUE 
#>  * Max possible chromatic volume: 0.215735 
#> 
#> 'avalue' automatically set to 2.6255e-01
#> 'avalue' automatically set to 2.4445e-02
#> 'avalue' automatically set to 1.6251e-01
#>   centroid.u centroid.s centroid.m centroid.l        c.vol    rel.c.vol
#> B 0.14091298 0.04946432  0.3838526  0.4257701 6.281511e-06 2.901306e-05
#> C 0.06947461 0.03144895  0.4054651  0.4936114 4.739152e-06 2.188920e-05
#> T 0.15368451 0.06413428  0.3766734  0.4055078 5.183721e-06 2.394258e-05
#>    colspan.m    colspan.v  huedisp.m    huedisp.v   mean.ra    max.ra
#> B 0.05758429 0.0013841927 0.06717740 0.0011466898 0.8021427 0.9039261
#> C 0.03193253 0.0003263454 0.06164553 0.0013887690 0.8742042 0.9061528
#> T 0.06171418 0.0012215063 0.05595025 0.0005378623 0.7434629 0.8816377
#>          a.vol
#> B 4.586381e-06
#> C 1.849436e-06
#> T 2.388383e-06