roboost_plsr: RoBoost-PLSR : Robust method for partial least squares...

In maxmetz/RoBoost-PLSR: RoBoost-PLSR : robust method or partial least saqures regression

Description

RoBoost-PLSR : Robust method for partial least squares regression

Usage

roboost_plsr(
  X,
  Y,
  ncomp,
  niter = 50,
  gamma = gamma,
  beta = beta,
  alpha = alpha,
  th = 1 - 10^-12
)

Arguments

X	Explanatory variables
Y	Explained Variables
ncomp	Number of latent variables
niter	Number of maximal iterations
gamma	parameters for leverage point
beta	parameters for Y-residuals
alpha	parameters for X-residuals

Examples

n <- 10
p <- 6
set.seed(1)
X <- matrix(rnorm(n * p, mean = 10), ncol = p)
y1 <- 100 * rnorm(n)
y2 <- 100 * rnorm(n)
Y <- cbind(y1, y2)
set.seed(NULL)
Xr <- X[1:8, ] ; Yr <- Y[1:8, ]
Xu <- X[9:10, ] ; Yu <- Y[9:10, ]
library(roboost)
ncomp = 3
alpha = Inf
beta  = Inf
gamma = Inf
mod = roboost_plsr(X = Xr,Y = Yr ,ncomp,gamma =gamma,beta=beta,alpha=alpha)
pred = predict_roboost_plsr(mod$fm,Xu)

maxmetz/RoBoost-PLSR documentation built on Dec. 21, 2021, 3:52 p.m.