View source: R/CV_L2E_sparse_dist.R
CV_L2E_sparse_dist | R Documentation |
CV_L2E_sparse_dist
performs k-fold cross-validation for robust sparse regression under the L2 criterion with
distance penalty
CV_L2E_sparse_dist( y, X, beta0, tau0, kSeq, rhoSeq, nfolds = 5, seed = 1234, method = "median", max_iter = 100, tol = 1e-04, trace = TRUE )
y |
Response vector |
X |
Design matrix |
beta0 |
Initial vector of regression coefficients, can be omitted |
tau0 |
Initial precision estimate, can be omitted |
kSeq |
A sequence of tuning parameter k, the number of nonzero entries in the estimated coefficients |
rhoSeq |
A sequence of tuning parameter rho, can be omitted |
nfolds |
The number of cross-validation folds. Default is 5. |
seed |
Users can set the seed of the random number generator to obtain reproducible results. |
method |
Median or mean to compute the objective |
max_iter |
Maximum number of iterations |
tol |
Relative tolerance |
trace |
Whether to trace the progress of the cross-validation |
Returns a list object containing the mean and standard error of the cross-validation error (vectors) – CVE and CVSE – for each value of k, the index of the k value with the minimum CVE and the k value itself (scalars), the index of the k value with the 1SE CVE and the k value itself (scalars), the sequence of rho and k used in the regression (vectors), and a vector listing which fold each element of y was assigned to
## Completes in 15 seconds set.seed(12345) n <- 100 tau <- 1 f <- matrix(c(rep(2,5), rep(0,45)), ncol = 1) X <- X0 <- matrix(rnorm(n*50), nrow = n) y <- y0 <- X0 %*% f + (1/tau)*rnorm(n) ## Clean Data k <- c(6,5,4) # (not run) # cv <- CV_L2E_sparse_dist(y=y, X=X, kSeq=k, nfolds=2, seed=1234) # (k_min <- cv$k.min) ## selected number of nonzero entries # sol <- L2E_sparse_dist(y=y, X=X, kSeq=k_min) # r <- y - X %*% sol$Beta # ix <- which(abs(r) > 3/sol$Tau) # l2e_fit <- X %*% sol$Beta # plot(y, l2e_fit, ylab='Predicted values', pch=16, cex=0.8) # points(y[ix], l2e_fit[ix], pch=16, col='blue', cex=0.8) ## Contaminated Data i <- 1:5 y[i] <- 2 + y0[i] X[i,] <- 2 + X0[i,] # (not run) # cv <- CV_L2E_sparse_dist(y=y, X=X, kSeq=k, nfolds=2, seed=1234) # (k_min <- cv$k.min) ## selected number of nonzero entries # sol <- L2E_sparse_dist(y=y, X=X, kSeq=k_min) # r <- y - X %*% sol$Beta # ix <- which(abs(r) > 3/sol$Tau) # l2e_fit <- X %*% sol$Beta # plot(y, l2e_fit, ylab='Predicted values', pch=16, cex=0.8) # points(y[ix], l2e_fit[ix], pch=16, col='blue', cex=0.8)
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