distplotsimple: displotsimple

In SCOUTer: Simulate Controlled Outliers

Description

Returns the distance plot directly providing the coordinates and Upper Control Limits.

Usage

distplotsimple(
  T2,
  SPE,
  lim.t2,
  lim.spe,
  ncomp,
  obstag = matrix(0, length(T2), 1),
  alpha = 0.05,
  plottitle = "Distance plot\n"
)

Arguments

T2	Vector with the Hotelling's T^2 values for each observation.
SPE	Vector with the SPE values for each observation.
lim.t2	Value of the Upper Control Limit for the T^2 statistic.
lim.spe	Value of the Upper Control Limit for the SPE.
ncomp	An integer indicating the number of PCs.
obstag	Optional column vector of integers indicating the group of each observation (0 or 1). Default value set to matrix(0, nrow(X), 1).
alpha	Optional number between 0 and 1 expressing the type I risk assumed in the computation of the Upper Control Limits (UCL) set to 0.05 (5 %) by default.
plottitle	Optional string with the plot title, "Distance plot" by default.

Details

Coordinates are expressed in terms of the Hotelling's T^2 (T^2, x-axis) and the Squared Prediction Error (SPE, y-axis). Observations can be identified by the obstag input argument.

Value

distplotobj ggplot object with the generated distance plot.

Examples

X <- as.matrix(X)
pcamodel.ref <- pcamb_classic(X[1:40,], 2, 0.05, "cent") # PCA-MB with first 40 
# observations
pcaproj <- pcame(X[-c(1:40),], pcamodel.ref) # Project last observations
distplotsimple(pcaproj$T2, pcaproj$SPE, pcamodel.ref$limt2, pcamodel.ref$limspe,
pcamodel.ref$ncomp)

pcaproj <- pcame(X, pcamodel.ref) # Project all observations
tags <- dotag(X[1:40,], X[-c(1:40),]) # 0's for observations used in PCA-MB
distplotsimple(pcaproj$T2, pcaproj$SPE, pcamodel.ref$limt2, pcamodel.ref$limspe, 
pcamodel.ref$ncomp, obstag = tags)

SCOUTer documentation built on July 1, 2020, 6:27 p.m.