---
title: "Cubist models"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Cubist models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(tidypredict)
library(Cubist)
library(dplyr)
```


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


## `tidypredict_` functions

```{r}
library(Cubist)
data("BostonHousing", package = "mlbench")

model <- Cubist::cubist(x = BostonHousing[, -14], y = BostonHousing$medv, committees = 3)
```

- Create the R formula
    ```{r}
tidypredict_fit(model)
    ```

- SQL output example
    ```{r}
tidypredict_sql(model, dbplyr::simulate_odbc())
    ```

- Add the prediction to the original table
    ```{r}
library(dplyr)

BostonHousing %>%
  tidypredict_to_column(model) %>%
  glimpse()
    ```

- Confirm that `tidypredict` results match to the model's `predict()` results
    ```{r}
tidypredict_test(model, BostonHousing)
    ```

## Parse model spec

Here is an example of the model spec:
```{r}
pm <- parse_model(model)
str(pm, 2)
```

```{r}
str(pm$terms[1:2])
```