gausspr objects are created from kernlab package, which
provides a means to do classification, regression, clustering,
novelty detection, quantile regression and dimensionality
reduction. Since fitted model objects from kernlab are S4,
the `butcher_gausspr`

class is not appended.

# S3 method for gausspr axe_call(x, verbose = FALSE, ...) # S3 method for gausspr axe_data(x, verbose = FALSE, ...) # S3 method for gausspr axe_env(x, verbose = FALSE, ...) # S3 method for gausspr axe_fitted(x, verbose = FALSE, ...)

x | A model object. |
---|---|

verbose | Print information each time an axe method is executed.
Notes how much memory is released and what functions are
disabled. Default is |

... | Any additional arguments related to axing. |

Axed gausspr object.

suppressWarnings(suppressMessages(library(kernlab))) test <- gausspr(Species ~ ., data = iris, var = 2)#> Using automatic sigma estimation (sigest) for RBF or laplace kernel#>#> ✖ Disabled: `print()`, `summary()`, `fitted()`#> ✖ Could not add 'butchered' class# Example with simulated regression data x <- seq(-20, 20, 0.1) y <- sin(x)/x + rnorm(401, sd = 0.03) test2 <- gausspr(x, y)#> Using automatic sigma estimation (sigest) for RBF or laplace kernel#> ✔ Memory released: '3,104 B'#> ✖ Disabled: `print()`, `summary()`, `fitted()`#> ✖ Could not add 'butchered' class