bootstrap_C.alpha: Alpha method to bootstrap a cutoff value to identify...

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

Description

Alpha method to bootstrap a cutoff value to identify anomalies

Usage

bootstrap_C.alpha(coeff, d, B, alpha, 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.
alpha	A value determining the percentage of rows to remove from coeff. alpha should be between (0, 1) with a suggested value of 0.05. Do not need to identify if c.method = "depth".
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 "alpha" c.method removes the alpha percent least deep coefficients. The rest of the coefficients are bootstrapped and 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.