View source: R/L2E_sparse_dist.R
L2E_sparse_dist | R Documentation |
L2E_sparse_dist
computes the solution path of the robust sparse regression under the L2 criterion with distance penalty
L2E_sparse_dist( y, X, beta0, tau0, kSeq, rhoSeq, stepsize = 0.9, sigma = 0.5, max_iter = 100, tol = 1e-04, Show.Time = 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 |
An increasing sequence of tuning parameter rho, can be omitted |
stepsize |
The stepsize parameter for the MM algorithm (0, 1) |
sigma |
The halving parameter sigma (0, 1) |
max_iter |
Maximum number of iterations |
tol |
Relative tolerance |
Show.Time |
Report the computing time |
Returns a list object containing the estimates for beta (matrix) and tau (vector) for each value of the tuning parameter k, the path of estimates for beta (list of matrices) and tau (matrix) for each value of rho, the run time (vector) for each k, and the sequence of rho and k used in the regression (vectors)
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 <- 5 sol <- L2E_sparse_dist(y=y, X=X, kSeq=k) r <- y - X %*% sol$Beta ix <- which(abs(r) > 3/sol$Tau) l2e_fit <- X %*% sol$Beta plot(y, l2e_fit, ylab='Predicted values', pch=16, cex=0.8) points(y[ix], l2e_fit[ix], pch=16, col='blue', cex=0.8) ## Contaminated Data i <- 1:5 y[i] <- 2 + y0[i] X[i,] <- 2 + X0[i,] sol <- L2E_sparse_dist(y=y, X=X, kSeq=k) r <- y - X %*% sol$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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