# Extract residuals estimate from the sparse version of the robregcc fitted object. Robust residuals estimate ```r ## S3 method for class 'robregcc' residuals(object, ...) ``` ## Arguments - `object`: robregcc fitted onject - `...`: Other argumnts for future usage. ## Returns residuals estimate ## Examples ```r library(magrittr) library(robregcc) data(simulate_robregcc) X <- simulate_robregcc$X; y <- simulate_robregcc$y C <- simulate_robregcc$C n <- nrow(X); p <- ncol(X); k <- nrow(C) Xt <- cbind(1,X) # accounting for intercept in predictor C <- cbind(0,C) # accounting for intercept in constraint bw <- c(0,rep(1,p)) # weight matrix to not penalize intercept example_seed <- 2*p+1 set.seed(example_seed) # Breakdown point for tukey Bisquare loss function b1 = 0.5 # 50% breakdown point cc1 = 1.567 # corresponding model parameter b1 = 0.25; cc1 = 2.937 # Initialization [PSC analysis for compositional data] control <- robregcc_option(maxiter=1000,tol = 1e-4,lminfac = 1e-7) fit.init <- cpsc_sp(Xt, y,alp = 0.4, cfac = 2, b1 = b1, cc1 = cc1,C,bw,1,control) ## Robust model fitting # control parameters control <- robregcc_option() beta.wt <- fit.init$betaR # Set weight for model parameter beta beta.wt[1] <- 0 control$gamma = 1 # gamma for constructing weighted penalty control$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 = 10 # number of fold of crossvalidation control$sigmafac = 2#1.345 # Robust regression using adaptive lasso penalty fit.ada <- robregcc_sp(Xt,y,C, beta.init = beta.wt, cindex = 1, gamma.init = fit.init$residuals, control = control, penalty.index = 1, alpha = 0.95) # Robust regression using lasso penalty [Huber equivalent] fit.soft <- robregcc_sp(Xt,y,C, cindex = 1, control = control, penalty.index = 2, alpha = 0.95) # Robust regression using hard thresholding penalty control$lmaxfac = 1e2 # Parameter for constructing sequence of lambda control$lminfac = 1e-3 # Parameter for constructing sequence of lambda control$sigmafac = 2#1.345 fit.hard <- robregcc_sp(Xt,y,C, beta.init = fit.init$betaf, gamma.init = fit.init$residuals, cindex = 1, control = control, penalty.index = 3, alpha = 0.95) residuals(fit.ada) residuals(fit.soft) residuals(fit.hard) ```

residuals function