xskel.plot {dplR} | R Documentation |
Skeleton Plot for Series and Master
Description
...
Usage
xskel.plot(rwl, series, series.yrs = as.numeric(names(series)),
win.start, win.end = win.start+100, n = NULL,
prewhiten = TRUE, biweight = TRUE)
Arguments
rwl |
a |
series |
a |
series.yrs |
a |
win.start |
year to start window |
win.end |
year to end window |
n |
|
prewhiten |
|
biweight |
|
Details
This function produces a plot that is a mix of a skeleton plot and a cross-correlation plot. It’s used in crossdating.
The top panel shows the normalized values for the master chronology (bottom half) and the series (top half) in green. The values are the detrended and standardized data (e.g., RWI).
Similarly, the black lines are a skeleton plot for the master and series with the marker years annotated for the master on the bottom axis and series on the top. The text at the top of the figure gives the correlation between the series and master (green bars) as well as the percentage of agreement between the years of skeleton bars for the series and master. I.e., if all the black lines occur in the same years the percentage would be 100%.
The bottom panels show cross correlations for the first half (left)
and second half of the time series using function ccf
as
ccf(x=series,y=master,lag.max=5)
.
The plot is built using the Grid package which
allows for great flexibility in building complicated plots. However,
these plots look best when they don’t cover too wide a range
of years (unless the plotting device is wider than is typical). For
that reason the user will get a warning if win.width
is
greater than 100 years.
Old-school skeleton plots to print on paper are made with skel.plot
.
Value
None. Invoked for side effect (plot).
Author(s)
Andy Bunn. Patched and improved by Mikko Korpela.
See Also
Examples
library(utils)
data(co021)
dat <- co021
#corrupt a series
bad.series <- dat$"641143"
names(bad.series) <- rownames(dat)
bad.series <- delete.ring(bad.series,year=1825)
# good match
xskel.plot(rwl=dat,series=bad.series,win.start=1850)
# overlap missing ring
xskel.plot(rwl=dat,series=bad.series,win.start=1800)