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
)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)