Description Usage Arguments Examples

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

1 2 | ```
mspSPEPlot(trainData, trainLabel, trainSPE, newData, newLabel, newSPE, trainObs,
var.amnt)
``` |

`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 |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
## 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. 17, 2017, 6:31 a.m.

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