Cross-correlation analysis.

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".

Value

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

Details

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

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