man/rmd/discrim_linear_sda.md

In parsnip: A Common API to Modeling and Analysis Functions

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 \pkg{dials} 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 \code{\link[=fit.model_spec]{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.

Prediction types

parsnip:::get_from_env("discrim_linear_predict") |>
  dplyr::filter(engine == "sda") |>
  dplyr::select(mode, type)

## # A tibble: 3 x 2
##   mode           type 
##   <chr>          <chr>
## 1 classification class
## 2 classification prob 
## 3 classification raw

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.

parsnip documentation built on Jan. 11, 2026, 9:06 a.m.