Every model has an implicit function whose output is the response variable and which has one
or more explanatory variables. (Exceptionally, there might be no explanatory variables as in
`response ~ 1`

.) One of the explanatory variables can be mapped to the horizontal axis; this can
be either quantitative or categorical. The remaining explanatory variables will be mapped to color,
facet-horizontal, and facet-vertical. For visual clarity, any quantitative
variables among these remaining variables must be coerced
to categorical, corresponding to a discrete set of colors and a discrete set of facets.

## Usage

```
model_plot(
mod,
nlevels = 3,
interval = c("confidence", "prediction", "none"),
level = 0.95,
palette = LETTERS[1:8],
model_ink = 0.7
)
```

## Arguments

- mod
A model object, made with

`model_train()`

,`lm()`

, or`glm()`

- nlevels
Integer. When quantitative variables need to be converted to factors for color or facetting, how many levels in those factors.

- interval
The type of interval to draw (default: confidence)

- level
The confidence or prediction level for the interval

- palette
One of "A" through "F" giving some control for people who don't like or can't see the default palette

- model_ink
The density of ink used to draw the model. ("alpha" for those in the know.)