multinom models

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

nnet::multinom() fits multinomial log-linear models. 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. The expressions implement the softmax over the per-class linear predictors, with the first level of the outcome acting as the reference class.

Since the output is a list, tidypredict_to_column() and tidypredict_test() are not supported.

`tidypredict_` functions

library(nnet)

model <- multinom(Species ~ ., data = iris, trace = FALSE)

Create the R formulas, one per class

fit <- tidypredict_fit(model)
names(fit)
#> [1] "setosa"     "versicolor" "virginica"
fit[["setosa"]]
#> 1/(1 + exp(18.6903742569434 + (Sepal.Length * -5.45842400699959) + 
#>     (Sepal.Width * -8.70740085056254) + (Petal.Length * 14.2447701274711) + 
#>     (Petal.Width * -3.09768387035736) - 0) + exp(-23.8362760290444 + 
#>     (Sepal.Length * -7.92363397245587) + (Sepal.Width * -15.3707689334044) + 
#>     (Petal.Length * 23.6597792429877) + (Petal.Width * 15.1353005479622) - 
#>     0))

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> 1.0000000, 0.9999996, 1.0000000, 0.9999968, 1.0000000, 1.…
#> $ versicolor   <dbl> 1.526406e-09, 3.536476e-07, 4.443506e-08, 3.163905e-06, 1…
#> $ virginica    <dbl> 2.716417e-36, 2.883729e-32, 6.103424e-34, 7.117010e-31, 1…

Confirm that the results match the model’s predict() results

probs <- sapply(fit, \(f) rlang::eval_tidy(f, iris))
all.equal(unname(probs), unname(predict(model, iris, type = "probs")))
#> [1] TRUE

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)

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

tidypredict_fit(p_model)[["virginica"]]
#> 1/(exp(0 - (-23.8362760290444 + (Sepal.Length * -7.92363397245587) + 
#>     (Sepal.Width * -15.3707689334044) + (Petal.Length * 23.6597792429877) + 
#>     (Petal.Width * 15.1353005479622))) + exp(18.6903742569434 + 
#>     (Sepal.Length * -5.45842400699959) + (Sepal.Width * -8.70740085056254) + 
#>     (Petal.Length * 14.2447701274711) + (Petal.Width * -3.09768387035736) - 
#>     (-23.8362760290444 + (Sepal.Length * -7.92363397245587) + 
#>         (Sepal.Width * -15.3707689334044) + (Petal.Length * 23.6597792429877) + 
#>         (Petal.Width * 15.1353005479622))) + 1)

Parse model spec

Here is an example of the model spec:

pm <- parse_model(model)
str(pm, 2)
#> List of 3
#>  $ general    :List of 4
#>   ..$ model  : chr "multinom"
#>   ..$ version: num 2
#>   ..$ type   : chr "multiclass_regression"
#>   ..$ family : chr "multinomial"
#>  $ classes    : chr [1:3] "setosa" "versicolor" "virginica"
#>  $ class_terms:List of 3
#>   ..$ :List of 1
#>   ..$ :List of 5
#>   ..$ :List of 5
#>  - attr(*, "class")= chr [1:3] "parsed_model" "pm_multiclass_regression" "list"

multinom models

tidypredict_ functions

parsnip

Parse model spec

`tidypredict_` functions