PlotStackCorrelation | R Documentation |
Plot the correlation of the spatial average of a certain number of proxy records with the underlying common signal depending on the number of records averaged and their temporal resolution (e.g., as in the firn core analysis of Münch and Laepple, 2018, Fig. 4).
PlotStackCorrelation(
data,
col.pal = NULL,
label = "",
xlim = NA,
ylim = NA,
xlab = "Number of cores",
ylab = "Averaging period (yr)",
xtm = NULL,
ytm = NULL,
xtl = NULL,
ytl = NULL,
xtm.min = NULL,
ytm.min = NULL
)
data |
a list of the correlation data (e.g. as output by
|
col.pal |
a color palette function to be used to assign colors in the
plot; the default |
label |
an optional label of the dataset to be displayed at the top of the plot. |
xlim |
the x limits (x1, x2) of the plot. Set to |
ylim |
as |
xlab |
x axis label. |
ylab |
y axis label. |
xtm |
x axis tick mark positions; default setting ( |
ytm |
y axis tick mark positions; default setting ( |
xtl |
x axis tick mark labels; if |
ytl |
equivalent to |
xtm.min |
x axis minor tick marks; default setting |
ytm.min |
as |
Thomas Münch
Münch, T. and Laepple, T.: What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores? Clim. Past, 14, 2053–2070, https://doi.org/10.5194/cp-14-2053-2018, 2018.
ObtainStackCorrelation
;
https://colorbrewer2.org/
# create a toy correlation dataset, which mimicks an increase
# in correlation with timescale and with the number of cores
# averaged, and plot it:
nf <- 20
nc <- 5
data <- list(
freq = seq(0.01, 0.5, length.out = nf),
correlation =
matrix(seq(0.05, 0.9, length.out = nf), nrow = nc, ncol = nf,
byrow = TRUE) +
matrix(seq(0, 0.3, length.out = nc), nrow = nc, ncol = nf) +
matrix(rnorm(nf * nc, sd = 0.02), nrow = nc, ncol = nf)
)
data$correlation[data$correlation > 1] <- 1
data$correlation[data$correlation < 0] <- 0
PlotStackCorrelation(data)
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