# details_discrim_linear_sparsediscrim: Linear discriminant analysis via regularization In parsnip: A Common API to Modeling and Analysis Functions

 details_discrim_linear_sparsediscrim R Documentation

## Linear discriminant analysis via regularization

### Description

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()
```
```## Linear Discriminant Model Specification (classification)
##
## Main Arguments:
##   regularization_method = character(0)
##
## Computational engine: sparsediscrim
##
## Model fit template:
## discrim::fit_regularized_linear(x = missing_arg(), y = missing_arg(),
##     method = character(0))
```

#### 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.

#### Case weights

The underlying model implementation does not allow for case weights.

#### 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.

parsnip documentation built on March 7, 2023, 5:57 p.m.