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| 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_ functionsCreate the R formulas, one per class
fit <- tidypredict_fit(model)
names(fit)
#> [1] "setosa" "versicolor" "virginica"
fit[["setosa"]]
#> 1/(1 + exp(-2.17403913501321 + (Sepal.Length * -1.38327577793295) +
#> (Sepal.Width * -4.20634050168117) + (Petal.Length * 4.57106120700443) +
#> (Petal.Width * 2.49705634843863) - (-13.1507094677663 + (Sepal.Length *
#> 5.73266109594567) + (Sepal.Width * 11.3604293693888) + (Petal.Length *
#> -16.8084846238172) + (Petal.Width * -16.6240117338794))) +
#> exp(-32.2372116751415 + (Sepal.Length * -4.34938531801267) +
#> (Sepal.Width * -7.15408886770749) + (Petal.Length * 12.2374234168127) +
#> (Petal.Width * 14.1269553854408) - (-13.1507094677663 +
#> (Sepal.Length * 5.73266109594567) + (Sepal.Width * 11.3604293693888) +
#> (Petal.Length * -16.8084846238172) + (Petal.Width * -16.6240117338794))))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> 1.009018e-20, 1.005223e-16, 2.186404e-18, 1.519958e-15, 4…
#> $ virginica <dbl> 3.704434e-39, 2.916177e-34, 2.957542e-36, 1.720915e-32, 1…Confirm that the results match the model’s predict()
results
sda() rounds its posterior probabilities with
zapsmall(), so expect agreement to about seven decimal
places rather than exactly.
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"]]
#> 1/(exp(-13.1507094677663 + (Sepal.Length * 5.73266109594567) +
#> (Sepal.Width * 11.3604293693888) + (Petal.Length * -16.8084846238172) +
#> (Petal.Width * -16.6240117338794) - (-32.2372116751415 +
#> (Sepal.Length * -4.34938531801267) + (Sepal.Width * -7.15408886770749) +
#> (Petal.Length * 12.2374234168127) + (Petal.Width * 14.1269553854408))) +
#> exp(-2.17403913501321 + (Sepal.Length * -1.38327577793295) +
#> (Sepal.Width * -4.20634050168117) + (Petal.Length * 4.57106120700443) +
#> (Petal.Width * 2.49705634843863) - (-32.2372116751415 +
#> (Sepal.Length * -4.34938531801267) + (Sepal.Width * -7.15408886770749) +
#> (Petal.Length * 12.2374234168127) + (Petal.Width * 14.1269553854408))) +
#> 1)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"]]
#> 1/(exp(-5.90478040863942 + (mpg * 0.450539182424721) + (ifelse(gear ==
#> "4", 1, 0) * 0.120334811858768) + (ifelse(gear == "5", 1,
#> 0) * -0.326397688436066) + (disp * -0.0361970033802603) -
#> (-9.69338951858341 + (mpg * -0.300898442298614) + (ifelse(gear ==
#> "4", 1, 0) * -0.28874400106328) + (ifelse(gear == "5",
#> 1, 0) * 0.34679228046224) + (disp * 0.0491641125277206))) +
#> exp(4.82341069251291 + (mpg * -0.145463097730621) + (ifelse(gear ==
#> "4", 1, 0) * 0.189561232611213) + (ifelse(gear == "5",
#> 1, 0) * -0.0358060646840223) + (disp * -0.0157055813201664) -
#> (-9.69338951858341 + (mpg * -0.300898442298614) + (ifelse(gear ==
#> "4", 1, 0) * -0.28874400106328) + (ifelse(gear ==
#> "5", 1, 0) * 0.34679228046224) + (disp * 0.0491641125277206))) +
#> 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 "sda"
#> ..$ 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"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.