plot.elnetfit {pense} | R Documentation |
Plot the coefficient path for a fitted elastic net regression model.
## S3 method for class 'elnetfit' plot(x, ...)
x |
a fitted EN model from |
... |
currently ignored. |
# Generate data with highly correlated groups of variables set.seed(12345) n <- 100 p <- 20 x <- 1 + matrix(rnorm(n * p), ncol = p) x[, 2] <- x[, 1] + rnorm(n, sd = 0.01) x[, 3] <- x[, 1] + rnorm(n, sd = 0.01) x[, 5] <- x[, 4] + rnorm(n, sd = 0.01) x[, 6] <- x[, 4] + rnorm(n, sd = 0.01) y <- x %*% c(rep(c(2, 5), each = 3), numeric(p - 6)) + rnorm(n) # Compute the classical EN est_en <- elnet( x, y, alpha = 0.5 ) # The `plot` method for the `elnet` output shows the coefficient paths plot(est_en) # --> from the paths it can be seen that the variables 1,2,3 and 4,5,6 are # grouped together # Compute the LASSO solution est_lasso <- elnet( x, y, alpha = 1 ) # The LASSO solution, on the other hand, can not unveil the grouping # structure plot(est_lasso) # Compute the Ridge solution est_ridge <- elnet( x, y, alpha = 0 ) # The Ridge also shows the grouping structure very nicely plot(est_ridge)