cpsc_sp: Principal sensitivity component analysis with compositional...

In robregcc: Robust Regression with Compositional Covariates

Description

Produce model and its residual estimate based on PCS analysis.

Usage

cpsc_sp(
  X0,
  y0,
  alp = 0.4,
  cfac = 2,
  b1 = 0.25,
  cc1 = 2.937,
  C = NULL,
  we,
  type,
  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
we	penalization index for model parameters beta
type	1/2 for l1 / l2 loss in the model
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

 

library(robregcc)
library(magrittr)

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

Xt <- cbind(1,X)  # include intercept in predictor
C <- cbind(0,C)    # include intercept in constraint
bw <- c(0,rep(1,p)) # weights 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 = 1000,
 tol = 1e-4,lminfac = 1e-7)
fit.init <- cpsc_sp(Xt, y,alp = 0.4, cfac = 2, b1 = b1,
cc1 = cc1,C,bw,1,control) 

robregcc documentation built on July 26, 2020, 1:07 a.m.