1d:T7b0,# Contribution Plots 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. ```r mspContributionPlot( trainData, trainLabel, newData, newLabel, var.amnt, trainObs ) ``` ## 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. - `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. ## Returns 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 ## 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) 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) ```

mspContributionPlot function