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Plotting radf models

Source: R/radf-autoplot.R
autoplot.radf_obj.Rd

autoplot.radf_obj takes radf_obj and radf_cv and returns a faceted ggplot object. shade is used as an input to shape_opt. shade modifies the geom_rect layer that demarcates the exuberance periods.

Usage

# S3 method for class 'radf_obj'
autoplot(
  object,
  cv = NULL,
  sig_lvl = 95,
  option = c("gsadf", "sadf"),
  min_duration = 0L,
  select_series = NULL,
  nonrejected = FALSE,
  shade_opt = shade(),
  trunc = TRUE,
  include_negative = "DEPRECATED",
  ...
)

# S3 method for class 'radf_obj'
autoplot2(
  object,
  cv = NULL,
  sig_lvl = 95,
  option = c("gsadf", "sadf"),
  min_duration = 0L,
  select_series = NULL,
  nonrejected = FALSE,
  trunc = TRUE,
  shade_opt = shade(),
  ...
)

shade(
  fill = "grey55",
  fill_negative = fill,
  fill_ongoing = NULL,
  opacity = 0.3,
  ...
)

Arguments

object

An object of class obj.

cv

An object of class cv.

sig_lvl

Significance level. It could be one of 90, 95 or 99.

option

Whether to apply the "gsadf" or "sadf" methodology (default = "gsadf"). Unlike datestamp, "svadf" is not supported here – it has no critical-value band to shade.

min_duration

The minimum duration of an explosive period for it to be reported (default = 0).

select_series

A vector of column names or numbers specifying the series to be used in plotting. Note that the order of the series does not alter the order used in plotting.

nonrejected

If TRUE, plot all variables regardless of rejecting the NULL at the 5 percent significance level.

shade_opt

Shading options, typically set using shade function.

trunc

Whether to remove the period of the minimum window from the plot (default = TRUE).

include_negative

Argument name is deprecated and substituted with nonrejected.

...

Further arguments passed to ggplot2::facet_wrap and ggplot2::geom_rect for shade.

fill

The shade color that indicates the exuberance periods with positive signal

fill_negative

The shade color that indicates the exuberance periods with positive signal

fill_ongoing

The shade color that indicates the exuberance periods that are ongoing the null hypothesis.

opacity

The opacity of the shade color aka alpha.

Value

A ggplot

Examples

# \donttest{
rsim_data <- radf(sim_data_wdate)
#> Using `date` as index variable.

autoplot(rsim_data)
#> Using `radf_crit` for `cv`.


# Modify facet_wrap options through ellipsis
autoplot(rsim_data, scales = "free_y", dir = "v")
#> Using `radf_crit` for `cv`.


# Modify the shading options
autoplot(rsim_data, shade_opt = shade(fill = "pink", opacity = 0.5))
#> Using `radf_crit` for `cv`.


# Allow for nonrejected series to be plotted
autoplot(rsim_data, nonrejected = TRUE)
#> Using `radf_crit` for `cv`.


# Remove the shading completely (2 ways)
autoplot(rsim_data, shade_opt = NULL)
#> Using `radf_crit` for `cv`.

autoplot(rsim_data, shade_opt = shade(opacity = 0))
#> Using `radf_crit` for `cv`.


# Plot only the series with the shading options
autoplot2(rsim_data)
#> Using `radf_crit` for `cv`.

autoplot2(rsim_data, trunc = FALSE) # keep the minw period
#> Using `radf_crit` for `cv`.


# We will need ggplot2 from here on out
library(ggplot2)

# Change (overwrite) color, size or linetype
autoplot(rsim_data) +
  scale_color_manual(values = c("black", "black")) +
  scale_size_manual(values = c(0.9, 1)) +
  scale_linetype_manual(values = c("solid", "solid"))
#> Using `radf_crit` for `cv`.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.
#> Scale for linetype is already present.
#> Adding another scale for linetype, which will replace the existing scale.


# Change names through labeller (first way)
custom_labels <- c("psy1" = "new_name_for_psy1", "psy2" = "new_name_for_psy2")
autoplot(rsim_data, labeller = labeller(.default = label_value, id = as_labeller(custom_labels)))
#> Using `radf_crit` for `cv`.


# Change names through labeller (second way)
custom_labels2 <- series_names(rsim_data)
names(custom_labels2) <- custom_labels2
custom_labels2[c(3, 5)] <- c("Evans", "Blanchard")
autoplot(rsim_data, labeller = labeller(id = custom_labels2))
#> Using `radf_crit` for `cv`.


# Or change names before plotting
series_names(rsim_data) <- LETTERS[1:5]
autoplot(rsim_data)
#> Using `radf_crit` for `cv`.


# Change Theme options
autoplot(rsim_data) +
  theme(legend.position = "right")
#> Using `radf_crit` for `cv`.

# }