ccf_plot: Cross-correlation analysis.

In bruceR: Broadly Useful Convenient and Efficient R Functions

Cross-correlation analysis.

Description

Plot the results of cross-correlation analysis using ggplot2 (rather than R base plot) for more flexible modification of the plot.

Usage

ccf_plot(
  formula,
  data,
  lag.max = 30,
  sig.level = 0.05,
  xbreaks = seq(-100, 100, 10),
  ybreaks = seq(-1, 1, 0.2),
  ylim = NULL,
  alpha.ns = 1,
  pos.color = "black",
  neg.color = "black",
  ci.color = "blue",
  title = NULL,
  subtitle = NULL,
  xlab = "Lag",
  ylab = "Cross-Correlation"
)

Arguments

formula	Model formula like y ~ x.
data	Data frame.
lag.max	Maximum time lag. Defaults to 30.
sig.level	Significance level. Defaults to 0.05.
xbreaks	X-axis breaks.
ybreaks	Y-axis breaks.
ylim	Y-axis limits. Defaults to NULL to automatically estimate.
alpha.ns	Color transparency (opacity: 0~1) for non-significant values. Defaults to 1 for no transparency (i.e., opaque color).
pos.color	Color for positive values. Defaults to "black".
neg.color	Color for negative values. Defaults to "black".
ci.color	Color for upper and lower bounds of significant values. Defaults to "blue".
title	Plot title. Defaults to an illustration of the formula.
subtitle	Plot subtitle.
xlab	X-axis title. Defaults to "Lag".
ylab	Y-axis title. Defaults to "Cross-Correlation".

Details

Significant correlations with negative time lags suggest shifts in a predictor precede shifts in an outcome.

Value

A gg object, which you can further modify using ggplot2 syntax and save using ggsave().

See Also

granger_test

Examples

# resemble the default plot output by `ccf()`
p1 = ccf_plot(chicken ~ egg, data=lmtest::ChickEgg)
p1

# a more colorful plot
p2 = ccf_plot(chicken ~ egg, data=lmtest::ChickEgg, alpha.ns=0.3,
              pos.color="#CD201F",
              neg.color="#21759B",
              ci.color="black")
p2

bruceR documentation built on June 22, 2024, 12:26 p.m.