plotPcaEns: Map ensemble pca loadings and plot PC timeseries

In nickmckay/GeoChronR: Tools to Analyze and Visualize Time-Uncertain Data

Map ensemble pca loadings and plot PC timeseries

Description

Map ensemble pca loadings and plot PC timeseries

Usage

plotPcaEns(
  ens.pc.out,
  TS,
  map.type = "line",
  which.pcs = c(1, 2),
  f = 0.2,
  high.color = "red",
  low.color = "blue",
  dot.size = 5,
  restrict.map.range = TRUE,
  shape.by.archive = TRUE,
  projection = "mollweide",
  bound.circ = TRUE,
  probs = c(0.025, 0.25, 0.5, 0.75, 0.975),
  which.leg = 1,
  legend.position = c(0.5, 0.5),
  color
)

Arguments

ens.pc.out	results of pcaEns()
TS	Timeseries object http://nickmckay.github.io/LiPD-utilities/r/index.html#what-is-a-time-series used in the pcaEns() analysis
map.type	"google" or "line"
which.pcs	vector of PCs to plot. Choose two. c(1,2) is default.
f	zoom buffer for plotting
high.color	color for the high end of the scale
low.color	color for the low end of the scale
dot.size	How big are the dots on the map
restrict.map.range	TRUE or FALSE. Trim the size of the map to the points, for "line" map type
shape.by.archive	TRUE or FALSE. Use archiveType to assign shapes.
projection	Map project. All options on: ?mapproject
bound.circ	For polar projects, draw a boundary circle? TRUE or FALSE
probs	quantiles to calculate and plot in the PC timeseries
which.leg	which map legend to include in the summary plot?
legend.position	Where to put the map legend?
color	deprecated. Use high.color and low.color instead

Value

A gridExtra ggplot object

Author(s)

Nick McKay

See Also

Other plot: plotChron(), plotChronEns(), plotChronEnsDiff(), plotCorEns(), plotHistEns(), plotLine(), plotModelDistributions(), plotPvalsEnsFdr(), plotRegressEns(), plotScatterEns(), plotScreeEns(), plotSpectraEns(), plotSpectrum(), plotSummary(), plotSummaryTs(), plotTimeseriesEnsLines(), plotTimeseriesEnsRibbons(), plotTimeseriesStack(), plotTrendLinesEns()

Other pca: ar1Surrogates(), createSyntheticTimeseries(), pcaEns(), plotScreeEns()

nickmckay/GeoChronR documentation built on April 9, 2024, 5:26 a.m.