whitening {plotKML}R Documentation

whitening

Description

Derives a ‘whitenned’ color based on the Hue-Saturation-Intensity color model. This method can be used to visualize uncertainty: the original color is leached proportionally to the uncertainty (white color indicates maximum uncertainty).

Usage

whitening(z, zvar, zlim = c(min(z, na.rm=TRUE), max(z, na.rm=TRUE)),  
  elim = c(.4,1), global.var = var(z, na.rm=TRUE), col.type = "RGB")

Arguments

z

numeric; target variable (e.g. predicted values)

zvar

numeric; prediction error (variance)

zlim

upper and lower limits for target variable

elim

upper and lower limits for the normalized error

global.var

global variance (either estimated from the data or specified)

col.type

characted; "RGB" or "HEX"

Details

The HSI is a psychologically appealing color model for visualization of uncertainty: hue is used to visualize values and whitening (paleness or leaching percentage) is used to visualize the uncertainty, or in other words the map is incomplete in the areas of high uncertainty. Unlike standard legends for continuous variables, this legend has two axis — one for value range and one for uncertainty range (see also kml_legend.whitening).
The standard range for elim is 0.4 and 1.0 (maximum). This assumes that a satisfactory prediction is when the model explains more than 85% of the total variation (normalized error = 40%). Otherwise, if the value of the normalized error get above 80%, the model accounts for less than 50% of variability.
Whitening is of special interest for visualization of the prediction errors in geostatistics. Formulas to derive the whitening color are explained in Hengl et al. (2004).

Author(s)

Tomislav Hengl and Pierre Roudier

References

See Also

kml_legend.whitening

Examples

whitening(z=15, zvar=5, zlim=c(10,20), global.var=7)
# significant color;
whitening(z=15, zvar=5, zlim=c(10,20), global.var=4)
# error exceeds global.var -> totally white;

[Package plotKML version 0.5-6 Index]