Outlier Detection using Functional Boxplot

Detects outliers based on the functional boxplot method. Curves that exceed the fence (1.5 times the central envelope width) at any point are flagged as outliers.

Usage

outliers.boxplot(
  fdataobj,
  prob = 0.5,
  factor = 1.5,
  depth.func = depth.MBD,
  ...
)

Arguments

fdataobj

An object of class 'fdata'.

prob

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

factor

Factor for fence calculation (default 1.5).

depth.func

Depth function to use. Default is depth.MBD.

...

Additional arguments passed to depth function.

Value

A list of class 'outliers.fdata' with components:

outliers

Indices of detected outliers

depths

Depth values for all curves

cutoff

Not used (for compatibility)

fdataobj

Original fdata object

See also

boxplot.fdata for functional boxplot visualization

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)

# Detect outliers
out <- outliers.boxplot(fd)
print(out)
#> Functional data outlier detection
#>   Number of observations: 30 
#>   Number of outliers: 2 
#>   Outlier indices: 29 30