robregcc (version 1.0)

cpsc_nsp: Principal sensitivity component analysis with compositional covariates in non-sparse setting.

Description

Produce model and its residual estimate based in PCS analysis.

Usage

cpsc_nsp(X0, y0, alp = 0.4, cfac = 2, b1 = 0.25, cc1 = 2.937,
  C = NULL, control = list())

Arguments

X0

CLR transformed predictor matrix.

y0

model response vector

alp

(0,0.5) fraction of data sample to be removed to generate subsample

cfac

initial value of shift parameter for weight construction/initialization

b1

tukey bisquare function parameter producing desired breakdown point

cc1

tukey bisquare function parameter producing desired breakdown point

C

sub-compositional matrix

control

a list of internal parameters controlling the model fitting

Value

betaf

TModel parameter estimate

residuals

residual estimate

References

Mishra, A., Mueller, C.,(2019) Robust regression with compositional covariates. In prepration. arXiv:1909.04990.

Examples

# NOT RUN {
 

library(robregcc)
library(magrittr)

data(simulate_robregcc_nsp)
X <- simulate_robregcc_nsp$X;
y <- simulate_robregcc_nsp$y
C <- simulate_robregcc_nsp$C
n <- nrow(X); p <- ncol(X); k <-  nrow(C)

# Predictor transformation due to compositional constraint:
# Equivalent to performing centered log-ratio transform 
Xt <- svd(t(C))$u %>% tcrossprod() %>% subtract(diag(p),.) %>% crossprod(t(X),.)
#
Xm <- colMeans(Xt)
Xt <- scale(Xt,Xm,FALSE)                  # centering of predictors 
mean.y <- mean(y)
y <- y - mean.y                           # centering of response 
Xt <- cbind(1,Xt)                         # accounting for intercept in predictor
C <- cbind(0,C)                           # accounting for intercept in constraint
bw <- c(0,rep(1,p))                       # weight matrix to not penalize intercept 

example_seed <- 2*p+1               
set.seed(example_seed) 

# Breakdown point for tukey Bisquare loss function 
b1 = 0.5                    # 50% breakdown point
cc1 =  1.567                # corresponding model parameter
# b1 = 0.25; cc1 =  2.937   

# Initialization [PSC analysis for compositional data]
control <- robregcc_option(maxiter=3000,tol = 1e-6)
fit.init  <- cpsc_nsp(Xt, y,alp=0.4,cfac=2,b1 = b1, cc1 = cc1,C,control)


# }