plotSpectraEns | R Documentation |
Plot the output of 'computeSpectraEns' as a ribbon plot of distributions, with specified confidence levels
plotSpectraEns(
spec.ens,
cl.df = NULL,
x.lims = NULL,
x.ticks = c(10, 20, 50, 100, 200, 500, 1000),
y.lims = NULL,
color.low = "white",
color.high = "grey70",
color.line = "Black",
color.cl = "red",
alp = 0.5
)
spec.ens |
list or dataframe containing frequency (freq) and power (pwr); typically output of computeSpectraEns |
cl.df |
list or dataframe containing confidence limits (90, 95 and 99%) as well as frequency (freq) |
x.lims |
2-element vector defining the range of periods (x-axis) |
x.ticks |
n-element vector of the periods labeled |
y.lims |
2-element vector defining the range of spectral power (y-axis) |
color.low |
Color of the outermost band; the extreme quantiles of the distribution |
color.high |
Color of the innermost band; the central quantiles of the distribution |
color.line |
Line color (following ggplot rules) |
color.cl |
color of the lines representing the confidence limits (90, 95, 99%) |
alp |
alpha (transparency) parameter for the ribbons |
'plotSpectraEns' re-uses 'plotTimeseriesRibbons' and therefore the same graphical conventions. Spectra are plotted on a log-log scale, with the x-axis labeled by periods instead of frequencies, for improved intelligibility.
a ggplot object
Julien Emile-Geay
Other plot:
plotChron()
,
plotChronEns()
,
plotChronEnsDiff()
,
plotCorEns()
,
plotHistEns()
,
plotLine()
,
plotModelDistributions()
,
plotPcaEns()
,
plotPvalsEnsFdr()
,
plotRegressEns()
,
plotScatterEns()
,
plotScreeEns()
,
plotSpectrum()
,
plotSummary()
,
plotSummaryTs()
,
plotTimeseriesEnsLines()
,
plotTimeseriesEnsRibbons()
,
plotTimeseriesStack()
,
plotTrendLinesEns()
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