acf_plot: ACF Plots

In rpruim/CalvinBayes: Support for Computational Bayesian Analysis Courses

Description

A wrapper around stats::acf() to make easy ACF plots in the ggplot2 system.

Usage

acf_plot(
  x,
  lag.max = NULL,
  type = c("correlation", "covariance", "partial"),
  plot = FALSE,
  na.action = na.fail,
  demean = TRUE,
  ...,
  color = "steelblue",
  fill = "black",
  alpha = 0.7,
  data = NULL
)

Arguments

x	a univariate or multivariate (not ccf) numeric time series object or a numeric vector or matrix, or an "acf" object.
lag.max	maximum lag at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series. Will be automatically limited to one less than the number of observations in the series.
type	character string giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial". Will be partially matched.
plot	A logical indicating whether the original base plot graphic should be displayed. Generally this should be left to its default value of FALSE.
na.action	function to be called to handle missing values. na.pass can be used.
demean	logical. Should the covariances be about the sample means?
...	further arguments to be passed to plot.acf.
color	color of reference lines (use "transparent" to hide)
fill	fill color for ACF plot
alpha	opacity of fill for ACF plot
data	a data frame (or other object) in which to evaluate x.

See Also

stats::acf()

Examples

acf_plot(rnorm(100))
acf_plot(rnorm(100), lag.max = 100, fill = "red", alpha = 0.3, color = "forestgreen")

rpruim/CalvinBayes documentation built on April 12, 2021, 1:49 p.m.