| Type: | Package |
| Title: | Relevance Vector Machine with Flexible Spike-and-Slab Priors |
| Version: | 0.1.0 |
| Description: | Fits Relevance Vector Machine (RVM) models for Normal, Epanechnikov, and Bickel prior distributions, and Gaussian or Mexican Hat kernels. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Config/roxygen2/version: | 8.0.0 |
| Imports: | ggplot2, kernlab, MASS |
| NeedsCompilation: | no |
| Packaged: | 2026-07-06 21:05:05 UTC; anaca |
| Author: | Alex Sousa [aut, cre], Rafael Oliveira [aut] |
| Maintainer: | Alex Sousa <asousa@unicamp.br> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-22 07:30:02 UTC |
Fit plot of an rvm_prior object
Description
S3 visualization method for objects of class "rvm_prior". Displays the training data, the predicted curve, the true function (if available), and the selected relevance vectors.
Usage
## S3 method for class 'rvm_prior'
plot(x, f_true = NULL, ...)
Arguments
x |
Object of class "rvm_prior". |
f_true |
Numeric vector or NULL. True function to overlay on the plot. |
... |
Additional arguments (ignored). |
Value
Invisible ggplot object. The plot is printed as a side effect.
Examples
dados <- sinc_data(n = 100, SNR = 7)
fit <- rvm_prior(dados$x, dados$y, prior = "Epanechnikov",
kernel = "gaussian", f_true = dados$f_true)
plot(fit)
Fits an RVM model with flexible prior
Description
Main function of the rvmprior package. Fits a Relevance Vector Machine (RVM) model for regression with support for three types of priors (Normal, Epanechnikov, Bickel) and two kernel types (gaussian, mexican_hat).
Usage
rvm_prior(
x,
y,
x_test = NULL,
prior = c("Normal", "Epanechnikov", "Bickel"),
kernel = c("gaussian", "mexican_hat"),
kernel_param = NULL,
prior_param = NULL,
alpha = 0.95,
show_plot = FALSE,
f_true = NULL,
...
)
Arguments
x |
Numeric vector. Predictor variable (unidimensional). |
y |
Numeric vector. Response variable. Must have the same length as x. |
x_test |
Numeric vector or NULL. Prediction points. If NULL, uses x. |
prior |
Character. Prior for the weights: "Normal", "Epanechnikov", or "Bickel". |
kernel |
Character. Kernel function: "gaussian" or "mexican_hat". |
kernel_param |
Numeric or NULL. Kernel parameter. If NULL, uses an automatic value. |
prior_param |
Numeric or NULL. Prior parameter. If NULL, uses an automatic value. |
alpha |
Numeric between 0 and 1. Spike probability. Default: 0.95. |
show_plot |
Logical. If TRUE, displays the fit plot. Default: FALSE. |
f_true |
Numeric vector or NULL. True function for comparison in the plot. |
... |
Additional arguments passed to rvm_spike_slab (iterations, tol). |
Value
Invisible object of class "rvm_prior" with the fit results.
Examples
dados <- sinc_data(n = 100, SNR = 7)
fit <- rvm_prior(dados$x, dados$y, prior = "Epanechnikov", kernel = "gaussian")
print(fit)
Generate simulated data from the sinc function with Gaussian noise
Description
The sinc function is defined as sinc(x) = sin(x)/x, with limit sinc(0) = 1. Used in nonparametric regression simulations.
Usage
sinc_data(n = 100, from = -10, to = 10, SNR = NULL, sd_noise = 0.2, seed = 123)
Arguments
n |
Integer. Number of points to generate. Default: 100. |
from |
Numeric. Lower bound of the x domain. Default: -10. |
to |
Numeric. Upper bound of the x domain. Default: 10. |
SNR |
Numeric or NULL. Desired signal-to-noise ratio. If NULL, uses sd_noise. |
sd_noise |
Numeric. Standard deviation of the noise. Used only when SNR is NULL. Default: 0.2. |
seed |
Integer. Seed for reproducibility. Default: 123. |
Value
An object of class "sinc_data" with elements x, y, f_true, sd_noise and SNR.
Examples
dados <- sinc_data(n = 100, SNR = 7)
print(dados)