Extensible Data Structures for Multivariate Analysis

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Documentation for package ‘multivarious’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

add_node	add a pre-processing stage
apply_rotation	Apply rotation
apply_transform	apply a pre-processing transform
bi_projector	Construct a bi_projector instance
bi_projector_union	A Union of Concatenated 'bi_projector' Fits
block_indices	get block_indices
block_lengths	get block_lengths
bootstrap	Bootstrap Resampling for Multivariate Models
bootstrap.pca	PCA Bootstrap Resampling
center	center a data matrix
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'
coef.cross_projector	Extract coefficients from a cross_projector object
colscale	scale a data matrix
components	get the components
compose_projector	Compose Two Projectors
compose_projectors	Projector Composition
concat_pre_processors	bind together blockwise pre-processors
convert_domain	Transfer data from one input domain to another via common latent space
cross_projector	Two-way (cross) projection to latent components
discriminant_projector	Construct a Discriminant Projector
fresh	Get a fresh pre-processing node cleared of any cached data
group_means	Compute column-wise mean in X for each factor level of Y
inverse_projection	Inverse of the Component Matrix
is_orthogonal	is it orthogonal
multiblock_biprojector	Create a Multiblock Bi-Projector
multiblock_projector	Create a Multiblock Projector
nblocks	get the number of blocks
ncomp	Get the number of components
nystrom_embedding	Nystrom method for out-of-sample embedding
partial_inverse_projection	Partial Inverse Projection of a Columnwise Subset of Component Matrix
partial_project	Partially project a new sample onto subspace
partial_projector	Construct a partial projector
partial_projector.projector	construct a partial_projector from a 'projector' instance
pass	a no-op pre-processing step
pca	Principal Components Analysis (PCA)
perm_ci	Permutation Confidence Intervals
predict.classifier	predict with a classifier object
prep	prepare a dataset by applying a pre-processing pipeline
prinang	Compute principal angles for a set of subspaces
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
project	New sample projection
project.cross_projector	project a cross_projector instance
projector	Construct a 'projector' instance
project_block	Project a single "block" of data onto the subspace
project_vars	Project one or more variables onto a subspace
reconstruct	Reconstruct the data
refit	refit a model
regress	Multi-output linear regression
reprocess	apply pre-processing parameters to a new data matrix
reprocess.cross_projector	reprocess a cross_projector instance
residualize	Compute a regression model for each column in a matrix and return residual matrix
residuals	Obtain residuals of a component model fit
reverse_transform	reverse a pre-processing transform
rf_classifier	construct a random forest wrapper classifier
rf_classifier.projector	create a random forest classifier
rotate	Rotate a Component Solution
scores	Retrieve the component scores
sdev	standard deviations
shape	Shape of the Projector
shape.cross_projector	shape of a cross_projector instance
standardize	center and scale each vector of a matrix
std_scores	Compute standardized component scores
svd_wrapper	Singular Value Decomposition (SVD) Wrapper
transpose	Transpose a model
truncate	truncate a component fit