plot.shrinkGPR_marg_samples_1D: Plot method for 1D marginal predictions

In shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Plot method for 1D marginal predictions

Description

Generates a plot of 1D conditional predictive samples produced by gen_marginal_samples with a single covariate.

Usage

## S3 method for class 'shrinkGPR_marg_samples_1D'
plot(x, ...)

Arguments

x	An object of class "shrinkGPR_marg_samples_1D", typically returned by gen_marginal_samples when providing a single covariate to sweep over.
...	Additional arguments passed to plot.mcmc.tvp for customizing the plot, such as axis labels or plotting options.

Details

By default, the function visualizes the posterior predictive median and 95% and 50% credible intervals for the selected covariate across a grid of evaluation points. Axis labels are automatically inferred if not explicitly provided.

Note: The shrinkTVP package must be installed to use this function.

Value

Called for its side effects. Returns invisible(NULL).

Author(s)

Peter Knaus [e-mail]

See Also

gen_marginal_samples

Other plotting functions: plot.shrinkGPR(), plot.shrinkGPR_marg_samples_2D(), plot.shrinkTPR()

Examples

# Simulate data
set.seed(123)
torch::torch_manual_seed(123)
n <- 100
x <- matrix(runif(n * 2), n, 2)
y <- sin(2 * pi * x[, 1]) + rnorm(n, sd = 0.1)
data <- data.frame(y = y, x1 = x[, 1], x2 = x[, 2])

# Fit GPR model
res <- shrinkGPR(y ~ x1 + x2, data = data)

# Generate marginal samples
marginal_samps_x1 <- gen_marginal_samples(res, to_eval = "x1", nsamp = 100)
marginal_samps_x2 <- gen_marginal_samples(res, to_eval = "x2", nsamp = 100)

# Plot marginal predictions
plot(marginal_samps_x1)
plot(marginal_samps_x2)

# Customize plot appearance (see plot.mcmc.tvp from shrinkTVP package for more options)
plot(marginal_samps_x2, shaded = FALSE, quantlines = TRUE, quantcol = "red")

shrinkGPR documentation built on Aug. 21, 2025, 5:43 p.m.