pramml: Partial Robust Adaptive Modified Maximum Likelihood

In rpls: Robust Partial Least Squares

Description

Robust Adaptive Modified Maximum Likelihood (RAMML) estimators can be used in the context of PLS to obtain scores and loadings in the latent regression model. The corresponding method is called Partial RAMML (PRAMML).

Usage

pramml(X, y, a, reg = "lts", pmml, opt = "l1m", usesvd = FALSE)

Arguments

X	predictor matrix
y	response variable
a	number of PLS components
reg	regression procedure to be used to compute initial estimate of parameter for the linearization of the intractable term; choices are LTS regression ("lts") and S regression ("s")
pmml	shape parameter of long-tailed symmetric distribution (considered as robustness tuning constant)
opt	if "l1m" the mean centering is done by the l1-median; otherwise if "median" the coordinate-wise median is taken
usesvd	if TRUE singular value decomposition is performed; logical, default is FALSE

Value

coef	vector with regression coefficients
intercept	coefficient for intercept
wy	vector of length(y) with residual weights
wt	vector of length(y) with weights for leverage
w	overall weights
scores	matrix with PLS X-scores
loadings	matrix with PLS X-loadings
fitted.values	vector with fitted y-values
loadings	column means of X
fitted.values	mean of y

Author(s)

Sukru Acitas <[e-mail]>

References

S. Acitas, Robust Statistical Estimation Methods for High-Dimensional Data with Applications, tech. rep., TUBITAK 2219, International Post Doctoral Research Fellowship Programme, 2019.

Examples

U <- c(rep(2,20), rep(5,30))
X <- replicate(6, U+rnorm(50))
beta <- c(rep(1, 3), rep(-1,3))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
res <- pramml(X, y, 4,"s", 16.5, opt ="l1m")

rpls documentation built on July 8, 2020, 6:46 p.m.