robregcc (version 1.0)

residuals: Extract residuals estimate from the sparse version of the robregcc fitted object.

Description

Robust residuals estimate

Usage

# S3 method for robregcc
residuals(object, ...)

Arguments

object

robregcc fitted onject

...

Other argumnts for future usage.

Value

residuals estimate

Examples

# NOT RUN {
 


library(robregcc)
library(magrittr)

data(simulate_robregcc_sp)
X <- simulate_robregcc_sp$X;
y <- simulate_robregcc_sp$y
C <- simulate_robregcc_sp$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   

# }
# NOT RUN {
# 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) 

# Robust procedure
# control parameters
control <- robregcc_option()
beta.wt <- fit.init$betaR           # Set weight for model parameter beta
beta.wt[1] <- 0
control$gamma = 2                   # gamma for constructing  weighted penalty
control$spb = 40/p                  # fraction of maximum non-zero model parameter beta
control$outMiter = 1000             # Outer loop iteration
control$inMiter = 3000              # Inner loop iteration
control$nlam = 50                   # Number of tuning parameter lambda to be explored
control$lmaxfac = 1                 # Parameter for constructing sequence of lambda
control$lminfac = 1e-8              # Parameter for constructing sequence of lambda 
control$tol = 1e-20;                # tolrence parameter for converging [inner  loop]
control$out.tol = 1e-16             # tolerence parameter for convergence [outer loop]
control$kfold = 5                   # number of fold of crossvalidation


# Robust regression using adaptive elastic net penalty [case III, Table 1]
fit.ada <- robregcc_sp(Xt,y,C, beta.init=fit.init$betaR, 
                       gamma.init = fit.init$residualR,
                       beta.wt=abs(beta.wt), 
                       gamma.wt = abs(fit.init$residualR),
                       control = control, 
                       penalty.index = 1, alpha = 0.95) 
                       
# Robust regression using lasso penalty [Huber equivalent]   [case II, Table 1]
fit.soft <- robregcc_sp(Xt,y,C, beta.init=NULL, gamma.init = NULL,
                        beta.wt=bw, gamma.wt = NULL,
                        control = control, penalty.index = 2, 
                        alpha = 0.95)


# Robust regression using hard thresholding penalty [case I, Table 1]
control$lmaxfac = 1e2        # Parameter for constructing sequence of lambda
control$lminfac = 1e-3       # Parameter for constructing sequence of lambda
fit.hard <- robregcc_sp(Xt,y,C, beta.init=fit.init$betaf, 
                        gamma.init = fit.init$residuals,
                        beta.wt=bw, gamma.wt = NULL,
                        control = control, penalty.index = 3, 
                        alpha = 0.95)
                        
                        
residuals(fit.ada)
residuals(fit.soft)
residuals(fit.hard)


# }