Construct a bi_projector instance

A bi_projector offers a two-way mapping from samples (rows) to scores and from variables (columns) to components. Thus, one can project from D-dimensional input space to d-dimensional subspace. And one can project (project_vars) from n-dimensional variable space to the d-dimensional component space. The singular value decomposition is a canonical example of such a two-way mapping.

Usage

bi_projector(v, s, sdev, preproc = prep(pass()), classes = NULL, ...)

Arguments

v

A matrix of coefficients with dimensions nrow(v) by ncol(v) (columns = components)

s

The score matrix

sdev

The standard deviations of the score matrix

preproc

(optional) A pre-processing pipeline, default is prep(pass())

classes

(optional) A character vector specifying the class attributes of the object, default is NULL

...

Extra arguments to be stored in the projector object.

Value

A bi_projector object

Examples

X <- matrix(rnorm(200), 10, 20)
svdfit <- svd(X)

p <- bi_projector(svdfit$v, s = svdfit$u %*% diag(svdfit$d), sdev=svdfit$d)
#> Warning: `prep()` was deprecated in multivarious 0.3.0.
#> ℹ Please use `fit()` instead.
#> ℹ The prep() function is deprecated. Use fit() for a more standard interface.
#> ℹ The deprecated feature was likely used in the multivarious package.
#>   Please report the issue to the authors.