pcame: pcame
In SCOUTer: Simulate Controlled Outliers

Description Usage Arguments Details Value Examples

Projection of X onto a Principal Component Analysis (PCA) model.

1	pcame(X, pcaref)

X

Matrix with observations that will be projected onto the PCA model.

pcaref

A list with the elemements of a PCA model:

m: mean.
s: standard deviation.
prepro: preprocessing: "none", "cent" or "autosc".
P: loading matrix.
lambda: vector with variances of each PC.

pcame performs the projection of the data in X onto the PCA model stored as a list of parameters. It returns the projection of the observations in X, along with the Squared Prediction Errors (SPE), Hotelling's T^2_A, contribution elements and the reconstruction of X obtained by the PCA model.

list with elements containing information about X in the PCA model:

Xpreprocessed: matrix X preprocessed.
Tscores: score matrix with the projection of X on each one of the A PCs.
E: error matrix with the par of X not explained by the PCA model.
SPE: vector with the SPE for each observation of X.
T2: vector with the T^_A for each observation of X.
T2matrix: matrix with the contributions of each PC to the T^2_A for each observation of X.
Xrec: matrix with the reconstructed part of X, i.e. the part of X explained by the PCA model.

X <- as.matrix(X)
pcamodel.ref <- pcamb_classic(X, 3, 0.1, "autosc") # PCA-MB with all observations
pcame(X, pcamodel.ref) # Project all observations onto PCA model of pcamodel.ref

pcamodel.ref <- pcamb_classic(X[1:40,], 2, 0.05, "cent") # PCA-MB with first 40 observations
pcame(X[-c(1:40),], pcamodel.ref) # Project observations not used in PCA-MB onto PCA model 
# of pcamodel.ref