geom_central_region: Central region plot
In GET: Global Envelopes

geom_central_region

R Documentation

Central region plot

Description

geom_central_region and stat_central_region can be used to compute and plot central_region from data arranged in a data.frame.

Usage

geom_central_region(
  mapping = NULL,
  data = NULL,
  stat = "CentralRegion",
  position = "identity",
  ...,
  coverage = 0.5,
  type = "erl",
  filled = TRUE,
  drawcenterline = TRUE,
  colours = grey.colors(length(coverage), start = 0.9, end = 0.5),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_central_region(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  coverage = 0.5,
  type = "erl",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`stat`	The statistical transformation to use on the data for this layer. When using a `⁠geom_*()⁠` function to construct a layer, the `stat` argument can be used the override the default coupling between geoms and stats. The `stat` argument accepts the following: A `Stat` ggproto subclass, for example `StatCount`. A string naming the stat. To give the stat as a string, strip the function name of the `stat_` prefix. For example, to use `stat_count()`, give the stat as `"count"`. For more information and other ways to specify the stat, see the layer stat documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`...`	Other arguments passed on to `layer()`'s `params` argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the `position` argument, or aesthetics that are required can not be passed through `...`. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, `colour = "red"` or `linewidth = 3`. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the `params`. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a `⁠stat_()⁠` function, the `...` argument can be used to pass on parameters to the `geom` part of the layer. An example of this is `stat_density(geom = "area", outline.type = "both")`. The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a `⁠geom_()⁠` function, the `...` argument can be used to pass on parameters to the `stat` part of the layer. An example of this is `geom_area(stat = "density", adjust = 0.5)`. The stat's documentation lists which parameters it can accept. The `key_glyph` argument of `layer()` may also be passed on through `...`. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
`coverage`	A number between 0 and 1. The 100*coverage% central region will be calculated. A vector of values can also be provided, leading to the corresponding number of central regions.
`type`	The options and details for `type` are given in `central_region`.
`filled`	Boolean. Should the ribbon be filled?
`drawcenterline`	Boolean. Should the center line be drawn?
`colours`	Colours for different coverage levels
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.

Details

Plots of central regions (global envelopes) with the specified coverage and type (see central_region). When splitting the set of functions to groups by aesthetics or facets, see examples, the central regions are constructed separately for each group, each having the specified coverage.

If Nfunc*(1-coverage) < 1, where Nfunc is the number of functions/curves, the curves are plotted instead of any region.

Aesthetics

geom_central_region requires x, y and curveid. Additionally geom_central_region uses the same aesthetics as geom_ribbon if filled==TRUE and geom_line otherwise. For multiple coverage values additional aesthetics are not currently supported.

Computed variables

stat_central_region computes after_stat(ymax) and after_stat(ymin) for the high and low value of the central region.

For multiple coverages the variables use the same names as central_region, i.e. hi.95 and lo.95 for the region with 95% coverage.

Examples

require("ggplot2")
## Generate some data
#------------------------------------------------------
# Simulate regression data according to the cubic model
# f(x) = 0.8x - 1.8x^2 + 1.05x^3 for x in [0,1]
par <- c(0,0.8,-1.8,1.05) # Parameters of the true polynomial model
res <- 100 # Resolution
x <- seq(0, 1, by=1/res); x2=x^2; x3=x^3;

f <- par[1] + par[2]*x + par[3]*x^2 + par[4]*x^3 # The true function
d <- f + rnorm(length(x), 0, 0.04) # Data

# Estimate polynomial regression model
reg <- lm(d ~ x + x2 + x3)
ftheta <- reg$fitted.values
resid0 <- reg$residuals

# Bootstrap regression
B <- 200 # Number of bootstrap samples
df <- NULL
for(i in 1:B) {
  u <- sample(resid0, size=length(resid0), replace=TRUE)
  reg1 <- lm((ftheta+u) ~ x + x2 + x3)
  df <- rbind(df, data.frame(y=reg1$fitted.values, x=x, i=i,
    g=ifelse(i<14, "A", "B"), g2=ifelse(i<100, "A", "B")))
}

ggplot(df) + geom_line(aes(x, y, group=i))
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i), coverage=0.50)
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i), coverage=0.50, filled=FALSE)
# Central regions for two groups as specified by 'g2'
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i, col=g2), coverage=0.90, filled=FALSE)
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i), coverage=0.90) + facet_wrap(vars(g2))


# Central regions with multiple coverage levels
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i), coverage=c(0.2,0.4,0.6)) +
  theme_minimal()
ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i), coverage=seq(0.1, 0.9, length=20),
  colours=rainbow(20))


# Colors for multiregions are not supported
ggplot(df) + geom_central_region(aes(x=x, y=y+0.1*(g2=="B"),
  curveid=i, col=as.factor(g2)), coverage=c(0.05, 0.2,0.4,0.6))

ggplot(df) + geom_central_region(aes(x=x, y=y, curveid=i),
  coverage=c(0.05, 0.2,0.4,0.6)) + facet_wrap(vars(g2))

# Using stat_central_region with geom_linerange and geom_rect
ggplot(df) +
  geom_linerange(aes(curveid=i, x=x, y=y, ymax=after_stat(ymax), ymin=after_stat(ymin),
               group=g2, col=factor(g2)),
               stat="central_region", coverage = 0.90, position=position_dodge(0.01))
ggplot(within(df, {x = x+0.004*(g2=="B")})) +
  geom_rect(aes(curveid=i, x=x, y=y, xmax=after_stat(x), xmin=after_stat(x+0.004),
              ymax=after_stat(ymax), ymin=after_stat(ymin), group=g2, fill=factor(g2)),
              stat="central_region", coverage = 0.90)

# Non-finite values are not supported
ggplot(within(df, {y = ifelse(runif(length(y)) < 0.001, Inf, y)})) +
  geom_central_region(aes(x=x, y=y, curveid=i))

GET documentation built on Sept. 11, 2024, 5:46 p.m.