pcamb_classic: pcamb_classic
In SCOUTer: Simulate Controlled Outliers

Description Usage Arguments Value Examples

Principal Component Analysis (PCA) model fitting according to a matrix X using singular value decomposition (svd)

1	pcamb_classic(X, ncomp, alpha, prepro)

`X`	Matrix with observations that will used to fit the PCA model.
`ncomp`	An integer indicating the number of PCs that the model will have.
`alpha`	A number between 0 and 1 indicating the type I risk assumed to calculate the Upper Control Limits (UCLs) for the Squared Prediction Error (SPE), the Hotelling's T^2_A and the scores. The confidence level of these limits will be `(1-alpha)*100`.
`prepro`	A string indicating the preprocessing to be performed on X. Its possible values are: `"none"`, for any preprocessing, `"cent"`, for a mean-centering, or `"autosc"`, for a mean-centering and unitary variance scaling (autoscaling).

list with elements containing information about PCA model:

m: mean vector.
s: standard deviation vector.
P: loading matrix with the loadings of each PC stored as columns.
Pfull: full loading matrix obtained by the svd,
lambda: vector with the variance of each PC.
limspe: Upper Control Limit for the SPE with a confidence level (1-alpha)*100 %.
limt2: Upper Control Limit for the T^2_A with a confidence level (1-alpha)*100 %.
limits_t: Upper control Limits for the scores with a confidence level (1-alpha)*100 %.
prepro: string indicating the type of preprocessing performed on X.
ncomp: number of PCs of the PCA model, A.
alpha: value of the type I risk assumed to calculate the Upper Control Limits of the SPE, T^2_A and scores.
n: dimension of the number of rows in X.
S: covariance matrix of X.

X <- as.matrix(X)
pcamodel.ref <- pcamb_classic(X, 3, 0.1, "autosc") # PCA-MB with all observations
pcamodel.ref <- pcamb_classic(X[1:40,], 2, 0.05, "cent") # PCA-MB with first 40 
# observations