continuum: The continuum regression (CR) algorithm

In JICO: Joint and Individual Regression

The continuum regression (CR) algorithm

Description

This function performs an iteration update of the JICO algorithm using the CR algorithm. Details can be found in Appendix B in the JICO paper: Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2022).

Usage

continuum(
  X,
  Y,
  lambda,
  gam,
  om,
  U_old = matrix(, nrow = nrow(X), ncol = 0),
  D_old = matrix(, nrow = 0, ncol = 0),
  V_old = matrix(, nrow = 0, ncol = 0),
  Z_old = matrix(, nrow = 0, ncol = 0),
  verbose = FALSE
)

Arguments

X	The input feature matrix
Y	The input response vector
lambda	Deprecated. Regularization parameter if L2 penalization is used for CR. JICO uses zero as default.
gam	The gamma parameter in the CR algorithm. Set gam=0 for OLS model, gam=0.5 for PLS model, gam >= 1e10 for PCR model
om	The desired number of weight vectors to obtain in the CR algorithm, i.e. the predefined rank of joint or individual component.
U_old	The given inputs U from the previous JICO iteration step
D_old	The given inputs D from the previous JICO iteration step
V_old	The given inputs V from the previous JICO iteration step
Z_old	The given inputs Z from the previous JICO iteration step
verbose	Boolean. If it's desired to print out intermediate outputs

Value

A list of CR outputs that serve as the input for the next JICO iteration

JICO documentation built on May 31, 2023, 6:34 p.m.