lda models

In tidypredict: Run Predictions Inside the Database

if (requireNamespace("MASS", 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 | ✔ |

MASS::lda() fits linear discriminant analysis models. Predicting with such a model projects the predictors onto the discriminant space and compares the result against each class centroid. That path is linear in the predictors, so it collapses into one linear predictor per outcome class, and the posterior probabilities are the softmax of those linear predictors.

Because these models predict one probability per outcome class, 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

Note that MASS is used with :: below rather than attached, because attaching it would mask dplyr::select().

model <- MASS::lda(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)) all.equal(unname(probs), unname(predict(model, iris)$posterior))

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)
library(discrim)

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

tidypredict_fit(p_model)[["virginica"]]

Parse model spec

Here is an example of the model spec:

pm <- parse_model(model)
str(pm, 2)

tidypredict documentation built on Oct. 18, 2026, 5:06 p.m.