Linear discriminant analysis via regularization
Source:R/discrim_linear_sparsediscrim.R
details_discrim_linear_sparsediscrim.Rd
Functions in the sparsediscrim package fit different types of linear discriminant analysis model that regularize the estimates (like the mean or covariance).
Details
For this engine, there is a single mode: classification
Tuning Parameters
This model has 1 tuning parameter:
regularization_method
: Regularization Method (type: character, default: ‘diagonal’)
The possible values of this parameter, and the functions that they execute, are:
"diagonal"
:sparsediscrim::lda_diag()
"min_distance"
:sparsediscrim::lda_emp_bayes_eigen()
"shrink_mean"
:sparsediscrim::lda_shrink_mean()
"shrink_cov"
:sparsediscrim::lda_shrink_cov()
Translation from parsnip to the original package
The discrim extension package is required to fit this model.
library(discrim)
discrim_linear(regularization_method = character(0)) %>%
set_engine("sparsediscrim") %>%
translate()
Preprocessing requirements
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via fit()
, parsnip will
convert factor columns to indicators.
Variance calculations are used in these computations so zero-variance predictors (i.e., with a single unique value) should be eliminated before fitting the model.
References
lda_diag()
: Dudoit, Fridlyand and Speed (2002) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, Journal of the American Statistical Association, 97:457, 77-87.lda_shrink_mean()
: Tong, Chen, Zhao, Improved mean estimation and its application to diagonal discriminant analysis, Bioinformatics, Volume 28, Issue 4, 15 February 2012, Pages 531-537.lda_shrink_cov()
: Pang, Tong and Zhao (2009), Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data. Biometrics, 65, 1021-1029.lda_emp_bayes_eigen()
: Srivistava and Kubokawa (2007), Comparison of Discrimination Methods for High Dimensional Data, Journal of the Japan Statistical Society, 37:1, 123-134.