# T-Squared Contribution Plots Plots a variation of the Hotelling's T-squared statistic to visualize the contribution of each variable to a fault. ```r 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 ```r ## 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) ```

mspT2Plot function