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. IfNULL
(the default), \(p - 1\) components will be returned.- sup_row
A
numeric
orlogical
vector specifying the indices of the supplementary rows (individuals).- sup_col
A
numeric
orlogical
vector specifying the indices of the supplementary columns (variables).- weight_row
A
numeric
vector specifying the active row (individual) weights. IfNULL
(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. IfNULL
(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)