| Principal component analysis {Compositional} | R Documentation |
Principal component analysis
Description
Principal component analysis.
Usage
logpca(x, center = TRUE, scale = TRUE, k = NULL, vectors = FALSE)
Arguments
x |
A matrix with the compositional data. Zero values are not allowed. |
center |
Do you want your data centered? TRUE or FALSE. |
scale |
Do you want each of your variables scaled, i.e. to have unit variance? TRUE or FALSE. |
k |
If you want a specific number of eigenvalues and eigenvectors set it here, otherwise all eigenvalues (and eigenvectors if requested) will be returned. |
vectors |
Do you want the eigenvectors be returned? By dafault this is FALSE. |
Details
The logarithm is applied to the compositional data and PCA is performed.
Value
A list including:
values |
The eigenvalues. |
vectors |
The eigenvectors. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
See Also
alfa.pca, alfa.pcr, kl.alfapcr
Examples
x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
a <- logpca(x)