Computes a principal components analysis based on the singular value decomposition.

## Usage

```
# S4 method for CompositionMatrix
pca(
object,
center = TRUE,
scale = TRUE,
rank = NULL,
sup_row = NULL,
sup_col = NULL,
weight_row = NULL,
weight_col = NULL
)
# S4 method for LogRatio
pca(
object,
center = TRUE,
scale = TRUE,
rank = NULL,
sup_row = NULL,
sup_col = NULL,
weight_row = NULL,
weight_col = NULL
)
```

## Arguments

- object
A LogRatio object.

- center
A

`logical`

scalar: should the variables be shifted to be zero centered?- scale
A

`logical`

scalar: should the variables be scaled to unit variance?- rank
An

`integer`

value specifying the maximal number of components to be kept in the results. If`NULL`

(the default), \(p - 1\) components will be returned.- sup_row
A

`numeric`

or`logical`

vector specifying the indices of the supplementary rows (individuals).- sup_col
A

`numeric`

or`logical`

vector specifying the indices of the supplementary columns (variables).- weight_row
A

`numeric`

vector specifying the active row (individual) weights. If`NULL`

(the default), uniform weights are used. Row weights are internally normalized to sum 1- weight_col
A

`numeric`

vector specifying the active column (variable) weights. If`NULL`

(the default), uniform weights (1) are used.

## Value

A dimensio::PCA object.

## Examples

```
## Coerce to compositional data
data("hongite")
coda <- as_composition(hongite)
## Centered log-ratio
clr <- transform_clr(coda)
## PCA
X <- pca(clr)
## Plot
plot_individuals(X)
```