PCA_model: PCA model for process monitor

In chengfeifan/monitor: Write Process monitoring method

Description

Calculate the principal component analysis for process monitor, and also find the squared prediction error (SPE) and Hotelling's T2 test statistic values for each observation in this data matrix.

Usage

PCA_model(data, kernel_num, ...)

Arguments

data	A centered-and-scaled data matrix
kernel_num	The number of principle component
...	Lazy dots for additional internal arguments

Value

A list of class "pca" with the following:

• projectionMatrix – the q eigenvectors corresponding to the q largest eigenvalues as a p x q projection matrix

• LambdaInv – the diagonal matrix of inverse eigenvalues

• SPE – the vector of SPE test statistic values for each of the n observations contained in "data"

• T2 – the vector of Hotelling's T2 test statistic for each of the same n observations

References

https://github.com/gabrielodom/mvMonitoring

chengfeifan/monitor documentation built on May 14, 2019, 2:29 p.m.