Linear discriminant analysis via James-Stein-type shrinkage estimation

sda::sda() can fit a linear discriminant analysis model that can fit models between classical discriminant analysis and diagonal discriminant analysis.

Details

For this engine, there is a single mode: classification

Tuning Parameters

This engine has no tuning parameter arguments in discrim_linear().

However, there are a few engine-specific parameters that can be set or optimized when calling set_engine():

• lambda: the shrinkage parameters for the correlation matrix. This maps to the parameter dials::shrinkage_correlation().

• lambda.var: the shrinkage parameters for the predictor variances. This maps to dials::shrinkage_variance().

• lambda.freqs: the shrinkage parameters for the class frequencies. This maps to dials::shrinkage_frequencies().

• diagonal: a logical to make the model covariance diagonal or not. This maps to dials::diagonal_covariance().

Translation from parsnip to the original package

The discrim extension package is required to fit this model.

library(discrim)

discrim_linear() %>% 
  set_engine("sda") %>% 
  translate()

## Linear Discriminant Model Specification (classification)
## 
## Computational engine: sda 
## 
## Model fit template:
## sda::sda(Xtrain = missing_arg(), L = missing_arg(), verbose = FALSE)

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

• Ahdesmaki, A., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. Preprint.