Central region plot
geom_central_region and stat_central_region can be used to compute and plot central_region from data arranged in a data.frame.
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 )
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:
Stat ggproto subclass, for example StatCount.stat_ prefix. For example, to use stat_count(), give the stat as "count".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:
position_jitter(). This method allows for passing extra arguments to the position.position_ prefix. For example, to use position_jitter(), give the position as "jitter"....: 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().
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.
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.
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.
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))
central_region for the basic computation and, geom_ribbon for the default base geom.