fdars (version 0.3.3)

outliers.thres.lrt: LRT Outlier Detection Threshold

Description

Computes the bootstrap threshold for LRT-based outlier detection. This is a highly parallelized Rust implementation providing significant speedup over pure R implementations.

Usage

outliers.thres.lrt(
  fdataobj,
  nb = 200,
  smo = 0.05,
  trim = 0.1,
  seed = NULL,
  percentile = 0.99
)

Value

The threshold value at the specified percentile.

Arguments

fdataobj

An object of class 'fdata'.

nb

Number of bootstrap replications (default 200).

smo

Smoothing parameter for bootstrap noise (default 0.05).

trim

Proportion of curves to trim for robust estimation (default 0.1).

seed

Random seed for reproducibility.

percentile

Percentile of bootstrap distribution to use as threshold (default 0.99, meaning 99th percentile). Lower values make detection more sensitive (detect more outliers).

Examples

t <- seq(0, 1, length.out = 50)
X <- matrix(0, 30, 50)
for (i in 1:30) X[i, ] <- sin(2*pi*t) + rnorm(50, sd = 0.1)
fd <- fdata(X, argvals = t)
thresh <- outliers.thres.lrt(fd, nb = 100)

# More sensitive detection (95th percentile)
thresh_sensitive <- outliers.thres.lrt(fd, nb = 100, percentile = 0.95)