model-fda.md
In tidypredict: Run Predictions Inside the Database

returns the right output

Code
  lapply(lps, round_print)
Output
  [[1]]
  [1] "-15.77129 + (Sepal.Length * 6.443631) + (Sepal.Width * 12.38706) + (Petal.Length * -17.29227) + (Petal.Width * -21.19393)"

  [[2]]
  [1] "-2.040818 + (Sepal.Length * -1.562448) + (Sepal.Width * -4.46535) + (Petal.Length * 4.791495) + (Petal.Width * 3.125087)"

  [[3]]
  [1] "-34.19971 + (Sepal.Length * -4.881183) + (Sepal.Width * -7.921708) + (Petal.Length * 12.50077) + (Petal.Width * 18.06885)"

unsupported fda fits are rejected

Code
  tidypredict_fit(poly_model)
Condition
  Error in `parse_model()`:
  ! Only `mda::polyreg()` fits with `degree = 1` are supported.
  i This model was fit with `degree = 2`.

Code
  tidypredict_fit(mars_model)
Condition
  Error in `parse_model()`:
  ! The `method` used to fit this mda model is not supported.
  i Only `mda::polyreg()` and `mda::gen.ridge()` are supported, not <mars>.

Code
  tidypredict_fit(mda_model)
Condition
  Error in `parse_model()`:
  ! `mda::mda()` models are not supported.
  i Only `mda::fda()` discriminant models are supported.

Code
  parse_model(mda_model)
Condition
  Error in `parse_model()`:
  ! `mda::mda()` models are not supported.
  i Only `mda::fda()` discriminant models are supported.

tidypredict_test errors for fda models

Code
  tidypredict_test(model, iris)
Condition
  Error in `tidypredict_test()`:
  ! `tidypredict_test()` does not support `mda::fda()` models.
  i Use `tidypredict_fit()` directly for multiclass predictions.

an ordered factor is rejected (#343)

Code
  tidypredict_fit(mda::fda(cyl ~ mpg + gear, data = df))
Condition
  Error in `acceptable_ordered()`:
  ! The treatment contrast is the only one supported at this time. Field(s) with an invalid contrast are: "gear".

inline functions in the formula are rejected

Code
  tidypredict_fit(model)
Condition
  Error in `acceptable_lm()`:
  x Functions inside the formula are not supported.
  i Functions detected: "log".  Use `dplyr` transformations to prepare the data.