pavo : p erceptual a nalysis, v isualization and o rganization of spectral colour data in r

pavo: Perceptual Analysis, Visualization and Organization of Spectral Colour Data in R

Workflow:

Organising (import, bin, trim, aggregate)

Visualising (overlay plot, stack plot, heatmap, aggregated plot)

Analysing (tristimulus variables, visual models)

V. 0.5-1

Example data set

Eurema heacbe Lomandra sp.30 ♂ 30 ♀ 50 leaves

Organising

Functions:getspec – importing spectra (e.g. ‘.ttt’)as.rspec – convert object to ‘rspec’, interpolate, trimaggspec – aggregate spectra by a functionplotsmooth – visually explore levels of LOESS smoothingprocspec – smoothing, normalising, trimming, binning, remove neg. values

Visualising

Function plot‘overlay’ – all spectra in one plot‘stack’ – individual stacked plots for comparison‘heatmap’ – heatmap (best for 3d data)

Function aggplotplot aggregated spectra – default mean ± s.d (can be customised)

Analysing

Trichromatic variables (hue, saturation, brightness)Function summary

Returns 28 (!) variables: know what you want

Hue (x 5)

Saturation (x 15)

Brightness (x 3)

Visual Modelling

Some popular approaches:

• Segment analysis (Endler 1990) – NB. Not really ‘visual’ • Receptor-noise (Vorobyev & Osorio 1998)• Colour hexagon (Chittka 1992) (coming)

• Tetrahedral colour-space (Endler & Mielke 2005, Goldsmith 1990, Stoddard & Prum 2008)

-Be aware of assumptions, limitations etc. & justify all choices. -Consider using multiple approaches & exploring effects of parameter variation (e.g. ‘noise’ where noise is uncertain)-Test your results where possible!

e.g Segment analysis (Endler 1990)

Function segclass

Eg. Receptor-noise (Vorobyev & Osorio 1998)

Q: Can this be seen against this by them

♂

♀

Predator

Friend

Another predator?

Eg. Receptor-noise (Vorobyev & Osorio 1998)

Function: vismodel

Quantum catch

x x

Stimulus Receptor sens.Illuminant

Eg. Receptor-noise (Vorobyev & Osorio 1998)Receptor adaptation

von-Kries correction -

Function: vismodel

Eg. Receptor-noise (Vorobyev & Osorio 1998)

Noise

Function: coldist

signal/noise ratio of receptor

relative density of receptor

Eg. Receptor-noise (Vorobyev & Osorio 1998)Calculating chromatic contrasts:

Eucilidean distance between points (Qi’s) weighted by noise (weber fraction)

Units = Just Noticeable Distances (JND’s)

Function: coldist

Dichromatic -

Eg. Receptor-noise (Vorobyev & Osorio 1998)

Function: coldist

Tetrachromat -

Trichromat -

Eg. Receptor-noise (Vorobyev & Osorio 1998)

Function: coldist

Achromatic contrastreceptor/s used in achromatic vision

Eg. Tetrahedral colour-space

Calculations:• Quantum catch as per receptor noise (inc. all specified

assumptions) with RELATIVE cone stimulation values instead of absolute

• Plot ‘em as co-ordinates• Descriptors of points (angles & euc. distance from achromatic

origin) are your colour variables (hue, saturation)

-Useful for all sorts of stuff, flexible (e.g. Endler & Mielke 2005 vs Stoddard & Prum 2008)-Cannot tell you about ‘discrimination’ as no measure of noise included-Chromatic only

Eg. Tetrahedral colour-space Functions:

vismodel – RELATIVE quantum catch in visual system

tcs – calculate tetrahedral coordinates using results of vismodel

dist – eucilidean distance between points in tetrahedron

Visualising:

tcsplot – 3d interactive plot

projplot – 2d projection plot

tcsvol – calculate volume overlap between clouds of points