mspSPEPlot: Squared Prediction Error Contribution Plots

In mvMonitoring: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

Squared Prediction Error Contribution Plots

Description

Plots a variation of the squared prediction error (SPE) statistic to visualize the contribution of each variable to a fault.

Usage

mspSPEPlot(
  trainData,
  trainLabel,
  trainSPE,
  newData,
  newLabel,
  newSPE,
  trainObs,
  var.amnt
)

Arguments

trainData	an xts data matrix containing the training observations
trainLabel	Class labels for the training data as a logical (two states only) or finite numeric (two or more states) vector or matrix column (not from a data frame) with length equal to the number of rows in “data." For data with only one state, this will be a vector of 1s.
trainSPE	the SPE values corresponding to the newLabel state calculated by mspTrain using the full training data with all variables included
newData	an xts data matrix containing the new observation
newLabel	the class label for the new observation
newSPE	the SPE value returned by mspMonitor using the full new observation with all variables included
trainObs	the number of observations upon which to train the algorithm. This will be split based on class information by a priori class membership proportions.
var.amnt	the energy proportion to preserve in the projection, which dictates the number of principal components to keep

Examples

## Not run: 
# Create some data
dataA1 <- mspProcessData(faults = "B1")
traindataA1 <- dataA1[1:8567,]

# Train on the data that should be in control
trainResults <- mspTrain(traindataA1[,-1], traindataA1[,1], trainObs = 4320)


# Lag an out of control observation
testdataA1 <- dataA1[8567:8568,-1]
testdataA1 <- lag.xts(testdataA1,0:1)
testdataA1 <- testdataA1[-1,]
testdataA1 <- cbind(dataA1[8568,1],testdataA1)

# Monitor this observation
monitorResults <- mspMonitor(observations = testdataA1[,-1],
                             labelVector = testdataA1[,1],
                             trainingSummary = trainResults$TrainingSpecs)


tD <- traindataA1[,-1]
tL <- traindataA1[,1]
nD <- testdataA1[,-1]
nL <- testdataA1[,1]
tO <- trainObs
vA <- 0.95
nSPE <- monitorResults$SPE
tSPE <- trainResults$TrainingSpecs[[nL]]$SPE

mspSPEPlot(tD,tL,tSPE,nD,nL,nSPE,tO,vA)

## End(Not run)

mvMonitoring documentation built on Nov. 22, 2023, 1:09 a.m.