Plot the restricted bridge cross-validation curve

Display validation mean squared error over the fitted penalty grid, with historical error bars, counts of nonzero slopes and both selected penalties.

Usage

# S3 method for class 'cv.rbridge'
plot(x, sign.lambda = 1, ...)

Arguments

x: Fitted CV object returned by cv.rbridge.
sign.lambda: Horizontal-axis direction. The default 1 plots log(lambda); -1 reverses it using -log(lambda). Use positive fitted penalties for the logarithmic axis.
...: Graphical arguments passed to the initial base-R plot() call, such as main, xlab, ylab or ylim.

Value

Draws the plot and returns NULL invisibly.

Details

Points show x$cve; bars span x$cvlo to x$cvup. The bars use the historical cvse calculation described in cv.rbridge, rather than conventional foldwise standard errors. The top axis shows full-data nonzero-slope counts, excluding the intercept. Vertical dotted lines mark lambda.min and lambda.1se; the lines coincide when both selectors choose the same fitted penalty. The curve is a diagnostic for the supplied grid and folds; its shape depends on the data and need not be U-shaped.

Author

Bahadir Yuzbasi
Maintainer: Bahadir Yuzbasi b.yzb@hotmail.com

Examples

# Reproducible synthetic demonstration: 100 observations, eight predictors.
set.seed(2019)
beta <- c(3, 1.5, 0, 0, 2, 0, 0, 0)
p <- length(beta)
beta <- matrix(beta, nrow = p, ncol = 1)
n <- 100
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- as.numeric(X %*% beta + rnorm(n))
# A user-specified sum of standardized slopes: b[1] + b[2] + b[5] = 6.5.
# This target is illustrative; it is not asserted to equal the generating sum.
R1.mat <- matrix(c(1, 1, 0, 0, 1, 0, 0, 0), nrow = 1)
r1.vec <- matrix(6.5, nrow = 1, ncol = 1)

# Five penalties keep this help example fast; the default is 100.
model1 <- cv.rbridge(X, y, q = 1, R = R1.mat, r = r1.vec, nlambda = 5,
                     nfolds = 3, num_threads = 1)
plot(model1, main = "Cross-validation over the fitted penalty grid")

# Reverse the log-penalty axis without refitting the model.
plot(model1, sign.lambda = -1)