plotmap: Plot a Map and Identify/Select Spectra in the Map `levelplot` functions for hyperSpec objects. An image or map of a summary value of each spectrum is plotted. Spectra may be identified by mouse click.

Description

The model can contain the special column name .wavelength to specify the wavelength axis.

Usage

plotmap(object, model = spc ~ x * y, func = mean,
    func.args = list(), ...)
  ## S3 method for class 'hyperSpec,missing':
levelplot(x, data, ...)
  ## S3 method for class 'formula,hyperSpec':
levelplot(x, data,
    transform.factor = TRUE, ..., contour = FALSE,
    useRaster = !contour)
  map.identify(object, model = spc ~ x * y,
    voronoi = FALSE, ..., tol = 0.02, warn = TRUE)
  plotvoronoi(object, model = spc ~ x * y,
    use.tripack = FALSE, mix = FALSE, ...)

Arguments

object,data

the hyperSpec object

model,x

formula specifying the columns of object that are to be displayed by levelplot

func,func.args

Before plotting, plotmap applies function func with the arguments given in the list func.args to each of the spectra. Thus a single summary value is displayed for each of the spectra.

This can be suppresse

voronoi

Should the plot for identifying spectra by mouse click be produced by plotmap (default) or plotvoronoi?

...

further arguments are passed down the call chain, and finally to levelplot

transform.factor

If the color-coded variable is a factor, should trellis.factor.key be used to compute the color coding and legend?

contour,useRaster

see levelplot

tol

tolerance for map.identify as fraction of the viewport (i.e. in "npc" units)

warn

should a warning be issued if no point is within the specified tolerance? See also details.

use.tripack

Whether package tripack should be used for calculating the voronoi polygons. If FALSE, package deldir is used instead. See details.

mix

For Voronoi plots using package tripack, I experienced errors if the data was spatially ordered. Randomly rearrangig the rows of the hyperSpec object circumvents this problem.

Value

map.identify returns a vector of row indices into object of the clicked points.
The other functions return a lattice object.

Details

plotmap, map.identify, and the levelplot methods internally use the same gateway function to levelplot. Thus transform.factor can be used with all of them and the panel function defaults to panel.levelplot.raster for all three. Two special column names, .rownames and .wavelength may be used.

levelplot plots the spectra matrix.

plotvoronoi calls plotmap with different default settings, namely the panel function defaults to panel.voronoi. panel.voronoi depends on either of the packages 'tripack' or 'deldir' being installed. For further information, please consult the help page of panel.voronoi. On the chondro data set, plotmap is roughly 5 times faster than plotvoronoi using tripack, and ca. 15 times faster than plotvoronoi using deldir. Package tripack, however, is free only for non-commercial use. Also, it seems that tripack version hang (R running at full CPU power, but not responding nor finishing the calculation) for certain data sets. In this case, mix = TRUE may help.

map.identify calls plotmap and plotvoronoi, respectively and waits for (left) mouse clicks on points. Other mouse clicks end the input.

Unlike panel.identify, the indices returned by map.identify are in the same order as the points were clicked. Also, multiple clicks on the same point are returned as multiple entries with the same index.

map.identify uses option debuglevel similar to spc.identify: debuglevel == 1 will plot the tolerance window if no data point was inside (and additionally labels the point) while debuglevel == 2 will always plot the tolerance window.

The map.sel.* functions offer further interactive selection, see map.sel.poly.

Examples

Run this code

vignette (plotting)
vignette (introduction)

levelplot (spc ~ y * x, chondro [,,1003]) # properly rotated
plotmap (chondro [,,1003])

# plot spectra matrix
levelplot (spc ~ .wavelength * t, laser, contour = TRUE, col = "#00000080")
# see also plotmat

plotmap (chondro, clusters ~ x * y)

# Voronoi plots
smpl <- sample (chondro, 300)
plotmap (smpl, clusters ~ x * y)
if (require (tripack))
    plotvoronoi (smpl, clusters ~ x * y)
if (require (deldir))
    plotvoronoi (smpl, clusters ~ x * y,
                 use.tripack = FALSE)

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