sparsediscrim models
In tidypredict: Run Predictions Inside the Database

if (requireNamespace("sparsediscrim", quietly = TRUE)) {
  library(tidypredict)
  library(dplyr)
  eval_code <- TRUE
} else {
  eval_code <- FALSE
}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = eval_code
)

| Function |Works| |---------------------------------------------------------------|-----| |tidypredict_fit(), tidypredict_sql(), parse_model() | ✔ | |tidypredict_to_column() | ✗ | |tidypredict_test() | ✗ | |tidypredict_interval(), tidypredict_sql_interval() | ✗ | |parsnip | ✔ |

sparsediscrim fits regularized linear discriminant analysis models. The four supported functions are lda_diag(), lda_shrink_mean(), lda_shrink_cov(), and lda_emp_bayes_eigen(), which are the models behind the "sparsediscrim" engine of discrim_linear() (regularization_method values "diagonal", "shrink_mean", "shrink_cov", and "min_distance").

All four share the same structure: one mean vector and prior per outcome class, plus a single pooled covariance shared by every class, and they only differ in how that covariance is regularized. The class posterior probabilities are therefore a softmax over one linear predictor per class, so tidypredict_fit() returns a named list of expressions, one for each class, rather than a single expression. Since the output is a list, tidypredict_to_column() and tidypredict_test() are not supported.

`tidypredict_` functions

model <- sparsediscrim::lda_diag(Species ~ ., data = iris)

Create the R formulas, one per class r fit <- tidypredict_fit(model) names(fit) fit[["setosa"]]
Add the predictions to the original table ```r library(dplyr)

iris %>% mutate(!!!tidypredict_fit(model)) %>% glimpse() ```

Confirm that the results match the model's predict() results r probs <- sapply(fit, \(f) rlang::eval_tidy(f, iris)) posterior <- predict(model, iris, type = "prob") all.equal(unname(probs), unname(as.matrix(posterior)))

Note that predict() with type = "prob" is not usable for lda_emp_bayes_eigen() (regularization_method = "min_distance") models: sparsediscrim builds a rank-deficient covariance for them and returns NaN for every row. The expressions that tidypredict_fit() generates use the model's own pseudo-inverse of that covariance, so they agree with the model's type = "class" predictions.

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)
library(discrim)

p_model <- discrim_linear(regularization_method = "shrink_mean") %>%
  set_engine("sparsediscrim") %>%
  fit(Species ~ ., data = iris)

tidypredict_fit(p_model)[["virginica"]]

These models are fit from a numeric matrix, so a model fit with the x/y interface can only refer to the matrix columns it was given. Categorical predictors work through the formula interface and through parsnip, both of which keep the formula around and let the dummy columns be written in terms of the original factors:

cars <- transform(mtcars, vs = factor(vs), gear = factor(gear))

c_model <- discrim_linear() %>%
  set_engine("sparsediscrim") %>%
  fit(vs ~ mpg + gear + disp, data = cars)

tidypredict_fit(c_model)[["1"]]