geom_central_region: Central region plot

View source: R/geom_central_region.R

geom_central_regionR 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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

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.

See Also

central_region for the basic computation and, geom_ribbon for the default base geom.

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))


myllym/GET documentation built on Feb. 4, 2024, 10:44 p.m.