Function reference • multivarious

Model construction

Functions that compute multivariate decompositions

pca(): Principal Components Analysis (PCA)

svd_wrapper(): Singular Value Decomposition (SVD) Wrapper

regress(): Multi-output linear regression

Model classes for multivariate decompositions and extension

Generic S3 classes use to represented multivariate model fits

projector(): Construct a projector instance

bi_projector(): Construct a bi_projector instance

bi_projector_union(): A Union of Concatenated bi_projector Fits

discriminant_projector(): Construct a Discriminant Projector

cross_projector(): Two-way (cross) projection to latent components

multiblock_biprojector(): Create a Multiblock Bi-Projector

multiblock_projector(): Create a Multiblock Projector

partial_projector(<projector>): construct a partial_projector from a projector instance

Model Fitting and Projections

Functions for fitting models and applying projections.

project(): New sample projection

residualize(): Compute a regression model for each column in a matrix and return residual matrix

partial_project(): Partially project a new sample onto subspace

partial_projector(): Construct a partial projector

project_block(): Project a single "block" of data onto the subspace

project_vars(): Project one or more variables onto a subspace

transpose(): Transpose a model

reconstruct(): Reconstruct the data

inverse_projection(): Inverse of the Component Matrix

partial_inverse_projection(): Partial Inverse Projection of a Columnwise Subset of Component Matrix

compose_projector(): Compose Two Projectors

compose_projectors(): Projector Composition

refit(): refit a model

nystrom_embedding(): Nystrom method for out-of-sample embedding

Cross Projection

Functions for creating and working with cross_projectors for two-way projection between two sets of variables or features.

project(<cross_projector>): project a cross_projector instance

coef(<cross_projector>): Extract coefficients from a cross_projector object

reprocess(<cross_projector>): reprocess a cross_projector instance

shape(<cross_projector>): shape of a cross_projector instance

Model Components and Properties

Functions for working with model components and properties.

components(): get the components

scores(): Retrieve the component scores

std_scores(): Compute standardized component scores

sdev(): standard deviations

ncomp(): Get the number of components

shape(): Shape of the Projector

is_orthogonal(): is it orthogonal

truncate(): truncate a component fit

block_lengths(): get block_lengths

block_indices(): get block_indices

nblocks(): get the number of blocks

prinang(): Compute principal angles for a set of subspaces

Rotation and Transformation

Functions for rotating and transforming model components.

rotate(): Rotate a Component Solution

apply_rotation(): Apply rotation

convert_domain(): Transfer data from one input domain to another via common latent space

Resampling and Confidence Intervals

Functions for bootstrapping and estimating confidence intervals.

bootstrap(): Bootstrap Resampling for Multivariate Models

bootstrap(<pca>): PCA Bootstrap Resampling

perm_ci(): Permutation Confidence Intervals

Classifier Construction

Functions for constructing classifiers.

classifier(): Construct a Classifier

classifier(<discriminant_projector>): Create a k-NN classifier for a discriminant projector

classifier(<multiblock_biprojector>): Multiblock Bi-Projector Classifier

classifier(<projector>): create classifier from a projector

rf_classifier(): construct a random forest wrapper classifier

rf_classifier(<projector>): create a random forest classifier

predict(<classifier>): predict with a classifier object

Model Diagnostics and Residuals

Functions for evaluating model fit and residuals.

residuals(): Obtain residuals of a component model fit

Pre-processing

Functions for pre-processing data and managing pipelines.

center(): center a data matrix

pass(): a no-op pre-processing step

standardize(): center and scale each vector of a matrix

colscale(): scale a data matrix

prep(): prepare a dataset by applying a pre-processing pipeline

fresh(): Get a fresh pre-processing node cleared of any cached data

reprocess(): apply pre-processing parameters to a new data matrix

apply_transform(): apply a pre-processing transform

reverse_transform(): reverse a pre-processing transform

init_transform(): initialize a transform

concat_pre_processors(): bind together blockwise pre-processors

add_node(): add a pre-processing stage

Other

Other functions

print(<bi_projector>): Pretty Print S3 Method for bi_projector Class

print(<bi_projector_union>): Pretty Print S3 Method for bi_projector_union Class

print(<classifier>): Pretty Print Method for classifier Objects

print(<composed_projector>): Pretty Print Method for composed_projector Objects

print(<multiblock_biprojector>): Pretty Print Method for multiblock_biprojector Objects

print(<projector>): Pretty Print Method for projector Objects

group_means(): Compute column-wise mean in X for each factor level of Y