regress: Multi-output linear regression

In bbuchsbaum/multivarious: Extensible Data Structures for Multivariate Analysis

Multi-output linear regression

Description

Fit a multivariate regression model for a matrix of basis functions, X, and a response matrix Y. The goal is to find a projection matrix that can be used for mapping and reconstruction.

Usage

regress(
  X,
  Y,
  preproc = NULL,
  method = c("lm", "enet", "mridge", "pls"),
  intercept = FALSE,
  lambda = 0.001,
  alpha = 0,
  ncomp = ceiling(ncol(X)/2),
  ...
)

Arguments

X	the set of independent (basis) variables
Y	the response matrix
preproc	the pre-processor (currently unused)
method	the regression method: lm, enet, mridge, or pls
intercept	whether to include an intercept term
lambda	ridge shrinkage parameter (for methods mridge and enet)
alpha	the elastic net mixing parameter if method is enet
ncomp	number of PLS components if method is pls
...	extra arguments sent to the underlying fitting function

Value

a bi-projector of type regress

Examples

# Generate synthetic data
Y <- matrix(rnorm(100 * 10), 10, 100)
X <- matrix(rnorm(10 * 9), 10, 9)
# Fit regression models and reconstruct the response matrix
r_lm <- regress(X, Y, intercept = FALSE, method = "lm")
recon_lm <- reconstruct(r_lm)
r_mridge <- regress(X, Y, intercept = TRUE, method = "mridge", lambda = 0.001)
recon_mridge <- reconstruct(r_mridge)
r_enet <- regress(X, Y, intercept = TRUE, method = "enet", lambda = 0.001, alpha = 0.5)
recon_enet <- reconstruct(r_enet)
r_pls <- regress(X, Y, intercept = TRUE, method = "pls", ncomp = 5)
recon_pls <- reconstruct(r_pls)

bbuchsbaum/multivarious documentation built on April 15, 2024, 3:33 a.m.