Title: | Model Wrappers for Discriminant Analysis |
Version: | 1.0.1 |
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>). |
License: | MIT + file LICENSE |
URL: | https://github.com/tidymodels/discrim, https://discrim.tidymodels.org/ |
BugReports: | https://github.com/tidymodels/discrim/issues |
Depends: | parsnip (≥ 0.2.0), R (≥ 3.4) |
Imports: | dials, rlang, stats, tibble, withr |
Suggests: | covr, dplyr, earth, ggplot2, klaR, knitr, MASS, mda, mlbench, modeldata, naivebayes, rmarkdown, sda, sparsediscrim (≥ 0.3.0), spelling, testthat (≥ 3.0.0), xml2 |
Config/Needs/website: | tidymodels/tidymodels, tidyverse/tidytemplate |
Encoding: | UTF-8 |
Language: | en-US |
LazyData: | true |
RoxygenNote: | 7.2.3 |
Config/testthat/edition: | 3 |
NeedsCompilation: | no |
Packaged: | 2023-03-08 20:38:54 UTC; emilhvitfeldt |
Author: | Emil Hvitfeldt |
Maintainer: | Emil Hvitfeldt <emil.hvitfeldt@posit.co> |
Repository: | CRAN |
Date/Publication: | 2023-03-08 22:00:15 UTC |
parsnip methods for discriminant analysis
Description
discrim offers various functions to fit classification models via the discriminant analysis.
Details
The model function works with the tidymodels infrastructure so that the model can be resampled, tuned, tided, etc.
Example
As an example, we’ll use a flexible discriminant analysis model of Hastie, Tibshirani, and Buja (1994). This fits a model that uses features generated by the multivariate adaptive regression spline (MARS) model of Friedman (1991). It is able to create class boundaries that are polygons and has built-in feature selection.
The parabolic
data from the modeldata package will be used to
illustrate:
library(tidymodels) library(discrim) tidymodels_prefer() theme_set(theme_bw()) data(parabolic, package = "modeldata")
To create the model, the discrim_flexible()
function is used along with an engine of "earth"
(which contains the
methods to use the MARS model). We’ll set the number of MARS terms to
use but this can be tuned via the methods in the tune package.
The fit()
function estimates the model. fit_xy()
can be used if one
does not wish to use the formula method.
fda_mod <- discrim_flexible(num_terms = 3) %>% # increase `num_terms` to find smoother boundaries set_engine("earth") %>% fit(class ~ ., data = parabolic) fda_mod
## parsnip model object ## ## Call: ## mda::fda(formula = class ~ ., data = data, method = earth::earth, ## nprune = ~3) ## ## Dimension: 1 ## ## Percent Between-Group Variance Explained: ## v1 ## 100 ## ## Training Misclassification Error: 0.136 ( N = 500 )
Now let’s plot the class boundary by predicting on a grid of points then creating a contour plot for the 50% probability cutoff.
parabolic_grid <- expand.grid(X1 = seq(-5, 5, length = 100), X2 = seq(-5, 5, length = 100)) parabolic_grid <- parabolic_grid %>% bind_cols( predict(fda_mod, parabolic_grid, type = "prob") ) ggplot(parabolic, aes(x = X1, y = X2)) + geom_point(aes(col = class), alpha = .5) + geom_contour(data = parabolic_grid, aes(z = .pred_Class1), col = "black", breaks = .5) + coord_equal()
Author(s)
Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID)
Authors:
Max Kuhn max@posit.co (ORCID)
Other contributors:
Posit Software, PBC [copyright holder, funder]
See Also
Useful links:
Report bugs at https://github.com/tidymodels/discrim/issues
Wrapper for sparsediscrim models
Description
Wrapper for sparsediscrim models
Usage
fit_regularized_linear(x, y, method = "diagonal", ...)
fit_regularized_quad(x, y, method = "diagonal", ...)
Arguments
x |
A matrix or data frame. |
y |
A factor column. |
method |
A character string. |
... |
Not currently used. |
Value
A sparsediscrim object
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 |
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()
Internal wrapper functions
Description
Internal wrapper functions
Usage
klar_bayes_wrapper(x, y, ...)
pred_wrapper(object, new_data, ...)
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
data(parabolic)
library(ggplot2)
ggplot(parabolic, aes(x = X1, y = X2, col = class)) +
geom_point(alpha = .5) +
theme_bw()