| 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_ functionsNote that MASS is used with :: below rather
than attached, because attaching it would mask
dplyr::select().
Create the R formulas, one per class
fit <- tidypredict_fit(model)
names(fit)
#> [1] "setosa" "versicolor" "virginica"
fit[["setosa"]]
#> 1/(1 + exp(-2.02197415376309 + (Sepal.Length * -1.53119918820707) +
#> (Sepal.Width * -4.37604347769127) + (Petal.Length * 4.69566530591248) +
#> (Petal.Width * 3.06258538965302) - (-15.477836726795 + (Sepal.Length *
#> 6.31475845867536) + (Sepal.Width * 12.1393171806029) + (Petal.Length *
#> -16.9464246511956) + (Petal.Width * -20.770054592318))) +
#> exp(-33.5376867395709 + (Sepal.Length * -4.7835592704683) +
#> (Sepal.Width * -7.76327370291159) + (Petal.Length * 12.2507593452831) +
#> (Petal.Width * 17.707469202665) - (-15.477836726795 +
#> (Sepal.Length * 6.31475845867536) + (Sepal.Width * 12.1393171806029) +
#> (Petal.Length * -16.9464246511956) + (Petal.Width * -20.770054592318))))Add the predictions to the original table
library(dplyr)
iris %>%
mutate(!!!tidypredict_fit(model)) %>%
glimpse()
#> Rows: 150
#> Columns: 8
#> $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
#> $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
#> $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
#> $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
#> $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…
#> $ setosa <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ versicolor <dbl> 3.896358e-22, 7.217970e-18, 1.463849e-19, 1.268536e-16, 1…
#> $ virginica <dbl> 2.611168e-42, 5.042143e-37, 4.675932e-39, 3.566610e-35, 1…Confirm that the results match the model’s predict()
results
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"]]
#> 1/(exp(-15.477836726795 + (Sepal.Length * 6.31475845867536) +
#> (Sepal.Width * 12.1393171806029) + (Petal.Length * -16.9464246511956) +
#> (Petal.Width * -20.770054592318) - (-33.5376867395709 + (Sepal.Length *
#> -4.7835592704683) + (Sepal.Width * -7.76327370291159) + (Petal.Length *
#> 12.2507593452831) + (Petal.Width * 17.707469202665))) + exp(-2.02197415376309 +
#> (Sepal.Length * -1.53119918820707) + (Sepal.Width * -4.37604347769127) +
#> (Petal.Length * 4.69566530591248) + (Petal.Width * 3.06258538965302) -
#> (-33.5376867395709 + (Sepal.Length * -4.7835592704683) +
#> (Sepal.Width * -7.76327370291159) + (Petal.Length * 12.2507593452831) +
#> (Petal.Width * 17.707469202665))) + 1)Here is an example of the model spec:
pm <- parse_model(model)
str(pm, 2)
#> List of 3
#> $ general :List of 4
#> ..$ model : chr "lda"
#> ..$ version: num 2
#> ..$ type : chr "multiclass_regression"
#> ..$ family : chr "multinomial"
#> $ classes : chr [1:3] "setosa" "versicolor" "virginica"
#> $ class_terms:List of 3
#> ..$ :List of 5
#> ..$ :List of 5
#> ..$ :List of 5
#> - attr(*, "class")= chr [1:3] "parsed_model" "pm_multiclass_regression" "list"