I'm trying to run a multinomial logistic regression in R using tidymodels but I can't convert my results to a tidy object. Here's a sample using the iris data set.
# Multinomial -----------------------------------------------------------------
# recipe
multinom_recipe <-
recipe(Species ~ Sepal.Length + Sepal.Width + Sepal.Length + Petal.Width, data = iris) %>%
step_relevel(Species, ref_level = "setosa")
# model
multinom_model <- multinom_reg() %>%
set_engine("nnet")
# make workflow
multinom_wf <-
workflow() %>%
add_model(multinom_model) %>%
add_recipe(multinom_recipe) %>%
fit(data = iris) %>%
tidy()
multinom_wf
The last step throws the following error:
Error in eval(predvars, data, env) : object '..y' not found
I thought it was bc the output of the fit(data = iris) is a workflow object, but this code seems to work fine when I don't use workflow (which is the whole point of using tidymodels) or if I fit a linear model.
# recipe
linear_recipe <-
recipe(Sepal.Length ~ Sepal.Width + Sepal.Length + Petal.Width, data = iris)
# model
linear_model <- linear_reg() %>%
set_engine("lm")
# make workflow
linear_wf <-
workflow() %>%
add_model(linear_model) %>%
add_recipe(linear_recipe) %>%
fit(data = iris) %>%
tidy()
linear_wf
Anyone have an idea as to what I'm missing or is this a bug?
It could be a clash with the call. We could change it to
multinom_wf$fit$fit$fit$call <- quote(nnet::multinom(formula = Species ~ ., data = iris, trace = FALSE))
multinom_wf %>%
tidy
-output
# A tibble: 8 x 6
y.level term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 versicolor (Intercept) 4.17 12.0 0.348 0.728
2 versicolor Sepal.Length 1.08 42.0 0.0258 0.979
3 versicolor Sepal.Width -9.13 81.5 -0.112 0.911
4 versicolor Petal.Width 20.9 14.0 1.49 0.136
5 virginica (Intercept) -16.0 12.1 -1.33 0.185
6 virginica Sepal.Length 2.37 42.0 0.0563 0.955
7 virginica Sepal.Width -13.9 81.5 -0.171 0.864
8 virginica Petal.Width 36.8 14.1 2.61 0.00916
where
multinom_wf <-
workflow() %>%
add_model(multinom_model) %>%
add_recipe(multinom_recipe) %>%
fit(data = iris)