Bivariate (Two-Dimensional) Confidence Region
Calculate an empirical confidence region for two variables, and optionally overlay the smooth polygon on a scatterplot.
conf2d(x, ...)
## S3 method for class 'formula'
conf2d(formula, data, subset, ...)
## Default S3 method:
conf2d(x, y, level=0.95, n=200, method="wand", shape=1, smooth=50,
plot=TRUE, add=FALSE, xlab=NULL, ylab=NULL, col.points="gray",
col="black", lwd=2, ...)
conf2d_int(x, y, surf, level, n) # internal functionx |
a vector of x values, or a data frame whose first two columns contain the x and y values. |
y |
a vector of y values. |
formula |
a |
data |
a |
subset |
an optional vector specifying a subset of observations to be used. |
level |
the proportion of points that should be inside the region. |
n |
the number of regions to evaluate, before choosing the region
that matches |
method |
kernel smoothing function to use: |
shape |
a bandwidth scaling factor, affecting the polygon shape. |
smooth |
the number of bins (scalar or vector of length 2), affecting the polygon smoothness. |
plot |
whether to plot a scatterplot and overlay the region as a polygon. |
add |
whether to add a polygon to an existing plot. |
xlab |
a label for the x axis. |
ylab |
a label for the y axis. |
col.points |
color of points. |
col |
color of polygon. |
lwd |
line width of polygon. |
... |
further arguments passed to |
surf |
a list whose first three elements are x coordinates, y coordinates, and a surface matrix. |
This function constructs a large number (n) of smooth polygons,
and then chooses the polygon that comes closest to containing a given
proportion (level) of the total points.
The conf2d function calls bkde2D or kde2d to
compute a smooth surface from x and y. If users already have
a smoothed surface to work from, the internal conf2d_int can be
used directly to find the empirical confidence region that matches
level best.
List containing five elements:
x |
x coordinates defining the region. |
y |
y coordinates defining the region. |
inside |
logical vector indicating which of the original data coordinates are inside the region. |
area |
area inside the region. |
prop |
actual proportion of points inside the region. |
The area of a bivariate region is analogous to the range of a
univariate interval. This allows a quantitative comparison of
different confidence regions.
Ellipses are a more restrictive approach to calculate an empirical bivariate confidence region. Smooth polygons make fewer assumptions about how x and y covary.
The conf2d and freq2d functions are closely related. The
advantage of conf2d is that it returns a region as a smooth
polygon. The advantage of freq2d is that it returns a set that
is guaranteed to contain the correct proportion of points, even for
spatially complex datasets.
Arni Magnusson and Julian Burgos, based on an earlier function by Gregory R. Warnes.
quantile is the corresponding univariate equivalent.
The distfree.cr package uses a different smoothing algorithm to calculate bivariate empirical confidence regions.
ci2d in the gplots package is a
predecessor of conf2d.
freq2d calculates a discrete frequency distribution for
two continuous variables.
r2d2-package gives an overview of the package.
conf2d(Ushape)$prop conf2d(saithe, pch=16, cex=1.2, col.points=rgb(0,0,0,0.1), lwd=3) # First surface, then region plot(saithe, col="gray") surf <- MASS::kde2d(saithe$Bio, saithe$HR, h=0.25, n=100) region <- conf2d_int(saithe$Bio, saithe$HR, surf, level=0.95, n=200) polygon(region, lwd=2)
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