In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("discrim_linear", "sda")

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

r uses_extension("discrim_linear", "sda", "classification")

library(discrim)

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

Preprocessing requirements

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.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.