# Coefficients for cv.RMSS Object `coef.cv.RMSS` returns the coefficients for a cv.RMSS object. ```r ## S3 method for class 'cv.RMSS' coef( object, h_ind = NULL, t_ind = NULL, u_ind = NULL, individual_models = FALSE, group_index = NULL, ... ) ``` ## Arguments - `object`: An object of class cv.RMSS. - `h_ind`: Index for robustness parameter. - `t_ind`: Index for sparsity parameter. - `u_ind`: Index for diversity parameter. - `individual_models`: Argument to determine whether the coefficients of each model are returned. Default is FALSE. - `group_index`: Groups included in the ensemble. Default setting includes all the groups. - `...`: Additional arguments for compatibility. ## Returns The coefficients for the cv.RMSS object. ## Examples ```r # Simulation parameters n <- 50 p <- 100 rho <- 0.8 rho.inactive <- 0.2 group.size <- 5 p.active <- 15 snr <- 2 contamination.prop <- 0.3 # Setting the seed set.seed(0) # Block Correlation sigma.mat <- matrix(0, p, p) sigma.mat[1:p.active, 1:p.active] <- rho.inactive for(group in 0:(p.active/group.size - 1)) sigma.mat[(group*group.size+1):(group*group.size+group.size), (group*group.size+1):(group*group.size+group.size)] <- rho diag(sigma.mat) <- 1 # Simulation of beta vector true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active)) # Setting the SD of the variance sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr)) # Simulation of test data m <- 2e3 x_test <- mvnfast::rmvn(m, mu = rep(0, p), sigma = sigma.mat) y_test <- x_test %*% true.beta + rnorm(m, 0, sigma) # Simulation of uncontaminated data x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat) y <- x %*% true.beta + rnorm(n, 0, sigma) # Contamination of data contamination_indices <- 1:floor(n*contamination.prop) k_lev <- 2 k_slo <- 100 x_train <- x y_train <- y beta_cont <- true.beta beta_cont[true.beta!=0] <- beta_cont[true.beta!=0]*(1 + k_slo) beta_cont[true.beta==0] <- k_slo*max(abs(true.beta)) for(cont_id in contamination_indices){ a <- runif(p, min = -1, max = 1) a <- a - as.numeric((1/p)*t(a) %*% rep(1, p)) x_train[cont_id,] <- mvnfast::rmvn(1, rep(0, p), 0.1^2*diag(p)) + k_lev * a / as.numeric(sqrt(t(a) %*% solve(sigma.mat) %*% a)) y_train[cont_id] <- t(x_train[cont_id,]) %*% beta_cont } # CV RMSS rmss_fit <- cv.RMSS(x = x_train, y = y_train, n_models = 3, h_grid = c(35), t_grid = c(6, 8, 10), u_grid = c(1:3), initial_estimator = "robStepSplitReg", tolerance = 1e-1, max_iter = 1e3, neighborhood_search = FALSE, neighborhood_search_tolerance = 1e-1, n_folds = 5, alpha = 1/4, gamma = 1, n_threads = 1) rmss_coefs <- coef(rmss_fit, h_ind = rmss_fit$h_opt, t_ind = rmss_fit$t_opt, u_ind = rmss_fit$u_opt, group_index = 1:rmss_fit$n_models) sens_rmss <- sum(which((rmss_coefs[-1]!=0)) <= p.active)/p.active spec_rmss <- sum(which((rmss_coefs[-1]!=0)) <= p.active)/sum(rmss_coefs[-1]!=0) rmss_preds <- predict(rmss_fit, newx = x_test, h_ind = rmss_fit$h_opt, t_ind = rmss_fit$t_opt, u_ind = rmss_fit$u_opt, group_index = 1:rmss_fit$n_models, dynamic = FALSE) rmss_mspe <- mean((y_test - rmss_preds)^2)/sigma^2 ``` ## See Also `cv.RMSS` ## Author(s) Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca

coef.cv.RMSS function