View source: R/mspContributionPlot.R

mspContributionPlot | R Documentation |

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
)
```

`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. |

`newData` |
an xts data matrix containing the new observation |

`newLabel` |
the class label for the new observation |

`var.amnt` |
the energy proportion to preserve in the projection, which dictates the number of principal components to keep |

`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. |

A contribution plot and a list with the following items:

- TrainCV –
A list vectors containing the contribution values corresponding to each observation in the set of training observations.

- NewCV –
The vector of contribution values associated with the new observation

```
## 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)
tD <- traindataA1[,-1]
tL <- traindataA1[,1]
nD <- testdataA1[,-1]
nL <- testdataA1[,1]
tO <- 4320
vA <- 0.95
mspContributionPlot(tD, tL, nD, nL, vA, tO)
## End(Not run)
```

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