sda models
In tidypredict: Run Predictions Inside the Database

if (requireNamespace("sda", 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 | ✔ |

sda::sda() fits shrinkage discriminant analysis models, including the diagonal variant (diagonal = TRUE). Predicting with such a model is a softmax over one linear predictor per outcome 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.

Only the features that survive shrinkage appear in the fitted model, and the generated expressions reference just those.

`tidypredict_` functions

model <- sda::sda(as.matrix(iris[1:4]), iris$Species, verbose = FALSE)

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, as.matrix(iris[1:4]), verbose = FALSE) all.equal(unname(probs), unname(posterior$posterior))

sda() rounds its posterior probabilities with zapsmall(), so expect agreement to about seven decimal places rather than exactly.

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)
library(discrim)

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

tidypredict_fit(p_model)[["virginica"]]

sda() is fit from a numeric matrix, so a model fit directly can only refer to the matrix columns it was given. Categorical predictors therefore work through the parsnip interface, which keeps the formula around and lets the dummy columns be written in terms of the original factors:

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

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

tidypredict_fit(c_model)[["8"]]