scoutsteps {SCOUTer}

scoutsteps

Description

Shift of an array following a step-wise pattern.

Usage

scoutsteps(
  X,
  pcaref,
  T2.target = NA,
  SPE.target = NA,
  nsteps = 1,
  gspe = 1,
  gt2 = 1
)

Arguments

X

Matrix with observations that will be shifted as rows.

pcaref

List with the elements of a PCA model:

• m: mean.

• s: standard deviation.

• prepro: preprocessing: "none", "cent" or "autosc".

• P: loading matrix.

• lambda: vector with variances of each PC.

T2.target

A number indicating the target value for the Hotelling's T^2_A after the shift. Set to NA by default.

SPE.target

A number indicating the target value for the Squared Prediction Error after the shift. Set to NA by default.

nsteps

A number indicating the number of steps between the reference and target values of the SPE and the T^2. Set to 1 by default.

gspe

A number indicating the term that will tune the spacing between steps for the SPE. Set to 1 by default (linear spacing).

gt2

A number indicating the term that will tune the spacing between steps for the SPE. Set to 1 by default (linear spacing).

Value

list with elements:

• X: matrix with the new and shifted data.

• SPE: SPE of each one of the generated outliers in the list element X.

• T2: T^2 of each one of the generated outliers in the list element X.

• step.spe: step of each observation according to the shift of the SPE.

• step.t2: step of each observation according to the shift of the T^2.

• tag: is a vector of ones as long as the number of generated observations.

Examples

X <- as.matrix(X)
pcamodel.ref <- pcamb_classic(X, 3, 0.1, "autosc") # PCA-MB with all observations
# Shift a set of observations increasing the T^2  and the SPE in 4 linear steps:
outsteps <- scoutsteps(X, pcamodel.ref, T2.target = matrix(40, nrow(X), 1), 
SPE.target = matrix(50, nrow(X), 1), nsteps = 4)
# Shift a set of observations increasing the SPE in 4 non-linear steps:
outsteps <- scoutsteps(X, pcamodel.ref, SPE.target = matrix(50, nrow(X), 1), nsteps = 4, 
gspe = 0.3)