bootstrap_C.depth: Bootstrap a cutoff value to identify anomalies

In cshannum/unequalgroupoutlier: Anomaly Detection in Unequal Length Functional Data (Title Case)

Description

Bootstrap a cutoff value to identify anomalies

Usage

bootstrap_C.depth(coeff, d, B, d.method)

Arguments

coeff	A dataframe of coefficients of interest. Each column is for the parameter to be estimate. Each row is the estimated parameters fore each curve. (No ID column here)
d	A vector of depths for all of the coefficients.
B	A value determining how many bootstrap datasets should be made to estimate the cutoff value with a suggested rate of 1000.
d.method	A character string determining the depth function to use: "LP", "Projection", "Mahalanobis", or "Euclidean". It is suggested to not use "Tukey" due to singularity in coefficient matrix. For details see depth

Details

The "depth" c.method bootstraps the coefficients with probability related to the original depth values. New depths are computed for each new bootstrapped set. The 1 1

Value

$Cb the cutoff value.

See Also

depth, bootstrap_C.alpha, and bootstrap_C.depth

cshannum/unequalgroupoutlier documentation built on May 13, 2019, 11:10 a.m.