Package 'discrim'

Title: Model Wrappers for Discriminant Analysis
Description: Bindings for additional classification models for use with the 'parsnip' package. Models include flavors of discriminant analysis, such as linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as well as naive Bayes classifiers (Hand and Yu (2007) <doi:10.1111/j.1751-5823.2001.tb00465.x>).
Authors: Emil Hvitfeldt [aut, cre] , Max Kuhn [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Emil Hvitfeldt <[email protected]>
License: MIT + file LICENSE
Version: 1.0.1.9000
Built: 2024-10-23 13:25:41 UTC
Source: https://github.com/tidymodels/discrim

Help Index

Parameter objects for Regularized Discriminant Models

Description

discrim_regularized() describes the effect of frac_common_cov() and frac_identity(). smoothness() is an alias for the adjust parameter in stats::density().

Usage

frac_common_cov(range = c(0, 1), trans = NULL)

frac_identity(range = c(0, 1), trans = NULL)

smoothness(range = c(0.5, 1.5), trans = NULL)

Arguments

range

A two-element vector holding the defaults for the smallest and largest possible values, respectively.
trans

A trans object from the scales package, such as scales::log10_trans() or scales::reciprocal_trans(). If not provided, the default is used which matches the units used in range. If no transformation, NULL.

Details

These parameters can modulate a RDA model to go between linear and quadratic class boundaries.

Value

A function with classes "quant_param" and "param"

Examples

frac_common_cov()

Parabolic class boundary data

Description

Parabolic class boundary data

Details

These data were simulated. There are two correlated predictors and two classes in the factor outcome.

Value

parabolic

a data frame

Examples

if (rlang::is_installed("ggplot2")) {
  data(parabolic)

  library(ggplot2)
  ggplot(parabolic, aes(x = X1, y = X2, col = class)) +
    geom_point(alpha = .5) +
    theme_bw()
}