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Plot autocorrelation and similar

Source: R/ggvar_acf.R, R/ggvar_ccf.R
ggvar_acf.Rd

Given a dataset or VAR model to get residuals from, ggvar_acf plots the auto-correlations (and similar) call for every series. ggvar_ccf plots all the cross-correlations (and similar) between the series, in a grid.

Usage

ggvar_acf(
  x,
  series = NULL,
  lag.max = NULL,
  type = "correlation",
  graph_type = "segment",
  args_type = list(),
  args_ribbon = list(linetype = 2, color = "blue", fill = NA),
  args_hline = list(yintercept = 0),
  args_labs = list(),
  args_facet = list(),
  ci = 0.95,
  ...
)

ggvar_ccf(
  x,
  series = NULL,
  lag.max = NULL,
  type = "correlation",
  graph_type = "segment",
  args_type = list(),
  args_ribbon = list(linetype = 2, color = "blue", fill = NA),
  args_hline = list(yintercept = 0),
  args_labs = list(),
  args_facet = list(),
  facet_type = "ggplot",
  ci = 0.95,
  ...
)

Arguments

x

A dataset (object coercible to "data.frame") or a "varest" object to get residuals from.

series

A character vector with series (variables) to consider. Defaults to all (NULL).

lag.max

The number of lags used to calculate the ACF, passed to acf.

type

The type of ACF to be computed. Can be either "correlation", "covariance", or "partial". Passed to acf.

graph_type

The ggplot geom used to create the plot: texts_vec.

args_type

Arguments passed to the 'geom' chosen in graph_type.

args_ribbon, args_hline

Additional arguments passed to geom_ribbon and geom_hline (respectively). See more in the 'Customization' section.

args_labs

Additional arguments passed to labs. If an empty list, will be changed to default values.

args_facet

Additional arguments passed to the faceting engine used.

ci

The confidence level for the confidence interval. Set to FALSE to omit. Used in geom_ribbon.

...

Arguments passed to methods, see the 'Methods' section.

facet_type

The facet 'engine' to be used. 'ggplot2' for ggplot2::facet_grid, 'ggh4x' for ggh4x::facet_grid2.

Value

A ggplot.

Details

Customization

The graph can be customized both with the 'static' arguments passed to each layer – using the args_* arguments –, and, if applicable, the 'dynamic' aesthetics – using the args_aes argument.

After built, the result can be further customized as any ggplot, adding or overwriting layers with the ggplot's +. It is useful to understand the data and the mappings coded by the package, using the function get_gg_info.

See vignette('customizing-graphs') for more details.

Methods

The data from x is extracted with the generic function texts_vec. Each class conditions an external function to pass the ... arguments to. Below there is a list with all the currently implemented classes:

See also

Other general time series plots: ggvar_distribution(), ggvar_history()

Other model diagnostics plots: ggvar_dispersion(), ggvar_distribution(), ggvar_history(), ggvar_select(), ggvar_stability()

Examples

ggvar_acf(freeny[-2], args_facet = list(scales = "free_y"))

ggvar_ccf(freeny[-2], args_facet = list(scales = "free_y"))

ggvar_acf(vars::VAR(freeny[-2]), args_facet = list(scales = "free_y"))