1d:T78e,# Principal sensitivity component analysis with compositional covariates in sparse setting. Produce model and its residual estimate based on PCS analysis. ```r 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 ## Returns - **betaf**: TModel parameter estimate - **residuals**: residual estimate ## Examples ```r 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) ``` ## References Mishra, A., Mueller, C.,(2019) **Robust regression with compositional covariates. In prepration**. arXiv:1909.04990.1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.

cpsc_sp function