fdars (version 0.3.3)

boxplot.fdata: Functional Boxplot

Description

Creates a functional boxplot for visualizing the distribution of functional data. The boxplot shows the median curve, central 50 percent envelope, fence (equivalent to whiskers), and outliers.

Usage

# S3 method for fdata
boxplot(
  x,
  prob = 0.5,
  factor = 1.5,
  depth.func = depth.MBD,
  show.outliers = TRUE,
  col.median = "black",
  col.envelope = "magenta",
  col.fence = "pink",
  col.outliers = "red",
  ...
)

Value

A list of class 'fbplot' with components:

median

Index of the median curve

central

Indices of curves in the central region

outliers

Indices of outlier curves

depth

Depth values for all curves

plot

The ggplot object

Arguments

x

An object of class 'fdata'.

prob

Proportion of curves for the central region (default 0.5 for 50 percent).

factor

Factor for fence calculation (default 1.5, as in standard boxplots).

depth.func

Depth function to use. Default is depth.MBD.

show.outliers

Logical. If TRUE (default), show outlier curves.

col.median

Color for median curve (default "black").

col.envelope

Color for central envelope (default "magenta").

col.fence

Color for fence region (default "pink").

col.outliers

Color for outlier curves (default "red").

...

Additional arguments passed to depth function.

Details

The functional boxplot (Sun & Genton, 2011) generalizes the standard boxplot to functional data using depth ordering:

• Median: The curve with maximum depth

• Central region: Envelope of curves with top 50 percent depth

• Fence: 1.5 times the envelope width beyond the central region

• Outliers: Curves that exceed the fence at any point

References

Sun, Y. and Genton, M.G. (2011). Functional boxplots. Journal of Computational and Graphical Statistics, 20(2), 316-334.

See Also

depth.MBD for the default depth function, outliers.boxplot for outlier detection using functional boxplots

Examples

# Create functional data with outliers
set.seed(42)
t <- seq(0, 1, length.out = 50)
X <- matrix(0, 30, 50)
for (i in 1:28) X[i, ] <- sin(2*pi*t) + rnorm(50, sd = 0.2)
X[29, ] <- sin(2*pi*t) + 2  # Magnitude outlier
X[30, ] <- cos(2*pi*t)       # Shape outlier
fd <- fdata(X, argvals = t)

# Create functional boxplot
fbp <- boxplot(fd)