The `plot`

method for `hyperSpec`

objects is a switchyard to `plot_spc()`

,
`plot_map()`

, and `plot_c()`

. The function also supplies some convenient
abbreviations for frequently used plots (see 'Details').

```
# S4 method for hyperSpec,missing
plot(x, y, ...)
# S4 method for hyperSpec,character
plot(x, y, ...)
```

- x
`hyperSpec`

object.- y
String (

`"spc"`

,`"map"`

, etc.) to select what type of plot should be produced. See section 'Details' for available values. If`y`

is missing,`plot(x)`

behaves like`plot(x, y = "spc")`

.- ...
Arguments passed to the respective plot function

Supported values for `y`

are:

- "spc" or nothing
calls

`plot_spc()`

to produce a spectra plot.- "spcmeansd"
plots mean spectrum +/- one standard deviation

- "spcprctile"
plots 16th, 50th, and 84th percentile spectra. If the distributions of the intensities at all wavelengths were normal, this would correspond to

`"spcmeansd"`

. However, this is frequently not the case. Then`"spcprctile"`

gives a better impression of the spectral data set.- "spcprctl5"
like

`"spcprctile"`

, but additionally the 5th and 95th percentile spectra are plotted.- "map"
calls

`plot_map()`

to produce a map plot.- "voronoi"
calls

`plot_voronoi()`

to produce a Voronoi plot (tessellated plot, like "map" for hyperSpec objects with uneven/non-rectangular grid).- "mat"
calls

`plot_matrix()`

to produce a plot of the spectra matrix (not to be confused with`graphics::matplot()`

).- "c"
calls

`plot_c()`

to produce a calibration (or time series, depth-profile, or the like).- "ts"
plots a time series: abbreviation for

`plot_c(x, use.c = "t")`

.- "depth"
plots a depth profile: abbreviation for

`plot_c(x, use.c = "z")`

.

`plot_spc()`

for spectra plots (intensity over wavelength),

`plot_map()`

for plotting maps, i.e. color coded summary value on two
(usually spatial) dimensions.

```
plot(flu)
plot(flu, "c")
#> Warning: Intensity at first wavelengh only is used.
plot(laser, "ts")
#> Warning: Intensity at first wavelengh only is used.
spc <- apply(faux_cell, 2, quantile, probs = 0.05)
spc <- sweep(faux_cell, 2, spc, "-")
plot(spc, "spcprctl5")
plot(spc, "spcprctile")
plot(spc, "spcmeansd")
### Use plot_spc() as a default plot function.
```