bruceR: ccf_plot – R documentation

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ccf_plot

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. Default is `30`.
`sig.level`	Significance level. Default is `0.05`.
`xbreaks`	X-axis breaks.
`ybreaks`	Y-axis breaks.
`ylim`	Y-axis limits. Default is `NULL` to automatically estimate.
`alpha.ns`	Color transparency (opacity: 0~1) for non-significant values. Default is `1` for no transparency (i.e., opaque color).
`pos.color`	Color for positive values. Default is `"black"`.
`neg.color`	Color for negative values. Default is `"black"`.
`ci.color`	Color for upper and lower bounds of significant values. Default is `"blue"`.
`title`	Plot title. Default is an illustration of the formula.
`subtitle`	Plot subtitle.
`xlab`	X-axis title. Default is `"Lag"`.
`ylab`	Y-axis title. Default is `"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().

Examples

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

# 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")

bruceR

Broadly Useful Convenient and Efficient R Functions

v0.6.2

GPL-3

Authors

Han-Wu-Shuang Bao [aut, cre]

Initial release

2021-04-08