Principal Component Analysis

Source: R/PCA.r

Function that performs principal component analysis on an abundance matrix.

PCA(x, cor, dim)

# S3 method for default
PCA(x, cor = FALSE, dim = min(nrow(x), ncol(x)))

# S3 method for Dataset
PCA(x, cor = FALSE, dim = min(nrow(x$Tab), ncol(x$Tab)))

Arguments

x

Numeric matrix where samples are columns and rows are species, or a Dataset object, see create_dataset.
cor

logical value indicating whether the correlation matrix should be used instead of the covariance matrix.
dim

Number of dimensions to return.

Value

A PCA object. Includes the same attributes as a pca object from the labdsv package. When the Dataset method is used, it includes two additional slots:

  • "Map"The Mapping file for the samples.

  • "Tax"The Taxonomic information of the taxa.

Details

This function is the same as function pca from the labdsv package, but includes a methdod for Dataset objects.

See also

create_dataset, pca, PCO, pco, plotgg.pca

Examples

data(Rhizo)
data(Rhizo.map)
Dat <- create_dataset(Rhizo,Rhizo.map)
Dat.pca <- PCA(Dat)
plotgg(Dat.pca,col="accession",shape="fraction",point_size=4,biplot=TRUE)
summary(Dat.pca)
#> Principal Component Analysis:
#> 	 69  Components
#> 
#>   Component Var.explained Cumulative
#> 1       PC1         41.43      41.43
#> 2       PC2         11.81      53.24
#> 3       PC3          7.15      60.39
#> 4       PC4          6.56      66.95
#> 5       PC5          6.18      73.13