pca: Perform Principal Component Analysis
In kimsche/MetaboMate: All you need for NMR-based metabolic profiling with R!

Description Usage Arguments Details Value Author(s) References See Also

This function is used to perform Principal Component Analysis (PCA).

1	pca(X, pc = 2, scale = "UV", center = T, method = "nipals")

`X`	Numeric input matrix with each row representing an observation and each column a metabolic feature.
`pc`	Desired number of principal components.
`scale`	Desired scaling method: `None`, `UV` (unit variance) or `Pareto` (Pareto scaling).
`center`	Logical indicating if data should be mean centered.
`method`	Algorithm for computing PCA. NIPALS is standard and usually fine. It can handle small amounts of missing/NA values.

Other methods include: 'svd', 'rnipals', 'bpca', 'ppca', 'svdImpute', 'robustPca', 'nlpca', 'llsImpute', 'llsImputeAll'. If these methods are specified, the pca function from the pcaMethods package is used to fit PCA model (see References).

This function returns a PCA_MetaboMate S4 object.

Torben Kimhofer tkimhofer@gmail.com

Geladi, P and Kowalski, B.R. (1986), Partial least squares and regression: a tutorial. Analytica Chimica Acta, 185, 1-17.

PCA_MetaboMate pca plotscores plotload opls