This function plots the contribution value for each variable of a newly monitored observation and compares them to the contribution values of the training data.
mspContributionPlot(
trainData,
trainLabel,
newData,
newLabel,
var.amnt,
trainObs
)an xts data matrix containing the training observations
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.
an xts data matrix containing the new observation
the class label for the new observation
the energy proportion to preserve in the projection, which dictates the number of principal components to keep
the number of observations upon which to train the algorithm. This will be split based on class information by a priori class membership proportions.
A contribution plot and a list with the following items:
A list vectors containing the contribution values corresponding to each observation in the set of training observations.
The vector of contribution values associated with the new observation
if (FALSE) {
# 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)
tD <- traindataA1[,-1]
tL <- traindataA1[,1]
nD <- testdataA1[,-1]
nL <- testdataA1[,1]
tO <- 4320
vA <- 0.95
mspContributionPlot(tD, tL, nD, nL, vA, tO)}