mspT2Plot: T-Squared Contribution Plots

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

T-Squared Contribution Plots

Description

Plots a variation of the Hotelling's T-squared statistic to visualize the contribution of each variable to a fault.

Usage

mspT2Plot(
  trainData,
  trainLabel,
  trainT2,
  newData,
  newLabel,
  newT2,
  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.
trainT2	the Hotelling's T-squared 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
newT2	the Hotelling's T-squared 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 <- 4320
vA <- 0.95
nT2 <- monitorResults$T2
tT2 <- trainResults$TrainingSpecs[[nL]]$T2

mspT2Plot(tD,tL,tT2,nD,nL,nT2,tO,vA)

## End(Not run)

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