Skip to contents

Plot historic values of dataset or VAR residuals

Source: R/ggvar_history.R
ggvar_history.Rd

Plots the historic values of variables in a dataset, or residuals of a VAR model. Can separate variables with facets or with a colors (see graph_type).

Usage

ggvar_history(
  x,
  series = NULL,
  index = NULL,
  faceted = TRUE,
  args_aes = list(),
  args_line = list(),
  args_labs = list(),
  args_facet = list(),
  ...
)

Arguments

x

Either a "varest" object for plotting the residuals, or an dataset (object coercible to data.frame) with numeric variables.

series

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

index

A vector of labels to the x-axis, normally dates. Must have length equal to x$obs or nrow(x). Defaults to a integer sequence.

faceted

Should the graph be divided in facets?. If true, args_aes is ignored.

args_aes

Defines aesthetics to differentiate the data. A named list of aesthetics names (*) – arguments passed to ggplot2::scale_*_manual. See more in the 'Customization' section.

args_line

Additional arguments passed to geom_line (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.

...

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

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.

The args_aes is a list with '* = arguments to scale_*_manual \ elements, where '*' represents the name of an aesthetic to apply to the \ data. View vignette('ggplot2-specs', 'ggplot2') to see the available \ aesthetics.

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 mtsdesc:::history_setup. Each class conditions an external function to pass the ... arguments to. Below there is a list with all the currently implemented classes:

  • Class 'varest': passed to nothing.

  • Class 'default': passed to nothing.

See also

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

Other historic values plots: ggvar_fit(), ggvar_predict()

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

Examples

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

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