l2e_regression_convex {L2E} | R Documentation |
L2E convex regression - PG
Description
l2e_regression_convex
performs L2E convex regression via block coordinate descent
with proximal gradient for updating both beta and tau.
Usage
l2e_regression_convex(y, b, tau, max_iter = 100, tol = 1e-04, Show.Time = TRUE)
Arguments
y |
Response vector |
b |
Initial vector of regression coefficients |
tau |
Initial precision estimate |
max_iter |
Maximum number of iterations |
tol |
Relative tolerance |
Show.Time |
Report the computing time |
Value
Returns a list object containing the estimates for beta (vector) and tau (scalar), the number of outer block descent iterations until convergence (scalar), and the number of inner iterations per outer iteration for updating beta and tau (vectors)
Examples
set.seed(12345)
n <- 200
tau <- 1
x <- seq(-2, 2, length.out=n)
f <- x^4 + x
y <- f + (1/tau) * rnorm(n)
## Clean data example
plot(x, y, pch=16, cex.lab=1.5, cex.axis=1.5, cex.sub=1.5, col='gray')
lines(x, f, lwd=3)
tau <- 1
b <- y
sol <- l2e_regression_convex(y, b, tau)
plot(x, y, pch=16, cex.lab=1.5, cex.axis=1.5, cex.sub=1.5, col='gray')
lines(x, f, lwd=3)
cvx <- fitted(cobs::conreg(y, convex=TRUE))
lines(x, cvx, col='blue', lwd=3)
lines(x, sol$beta, col='dark green', lwd=3)
## Contaminated data example
ix <- 0:9
y[45 + ix] <- 14 + rnorm(10)
plot(x, y, pch=16, cex.lab=1.5, cex.axis=1.5, cex.sub=1.5, col='gray')
lines(x, f, lwd=3)
tau <- 1
b <- y
sol <- l2e_regression_convex(y, b, tau)
plot(x, y, pch=16, cex.lab=1.5, cex.axis=1.5, cex.sub=1.5, col='gray')
lines(x, f, lwd=3)
cvx <- fitted(cobs::conreg(y, convex=TRUE))
lines(x, cvx, col='blue', lwd=3)
lines(x, sol$beta, col='dark green', lwd=3)
[Package L2E version 2.0 Index]