rpls (version 0.6.0)

pramml: Partial Robust Adaptive Modified Maximum Likelihood

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

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

# NOT RUN {
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")
# }