This function plots contrasts (y-axis) against values of predictor(s) variable(s) (x-axis and colors). This is especially useful in models with interactions, where the values of contrasts depend on the values of "condition" variables.

```
plot_cco(
model,
effect = NULL,
condition = NULL,
type = "response",
vcov = NULL,
conf_level = 0.95,
transform_pre = "difference",
transform_post = NULL,
draw = TRUE,
...
)
```

- model
Model object

- effect
Name of the variable whose contrast we want to plot on the y-axis

- condition
String or vector of two strings. The first is a variable name to be displayed on the x-axis. The second is a variable whose values will be displayed in different colors. Other numeric variables are held at their means. Other categorical variables are held at their modes.

- type
string indicates the type (scale) of the predictions used to compute marginal effects or contrasts. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message.

- vcov
Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:

FALSE: Do not compute standard errors. This can speed up computation considerably.

TRUE: Unit-level standard errors using the default

`vcov(model)`

variance-covariance matrix.String which indicates the kind of uncertainty estimates to return.

Heteroskedasticity-consistent:

`"HC"`

,`"HC0"`

,`"HC1"`

,`"HC2"`

,`"HC3"`

,`"HC4"`

,`"HC4m"`

,`"HC5"`

. See`?sandwich::vcovHC`

Heteroskedasticity and autocorrelation consistent:

`"HAC"`

Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger"

Other:

`"NeweyWest"`

,`"KernHAC"`

,`"OPG"`

. See the`sandwich`

package documentation.

One-sided formula which indicates the name of cluster variables (e.g.,

`~unit_id`

). This formula is passed to the`cluster`

argument of the`sandwich::vcovCL`

function.Square covariance matrix

Function which returns a covariance matrix (e.g.,

`stats::vcov(model)`

)

- conf_level
numeric value between 0 and 1. Confidence level to use to build a confidence interval.

- transform_pre
string or function. How should pairs of adjusted predictions be contrasted?

string: shortcuts to common contrast functions.

Supported shortcuts strings: difference, differenceavg, dydx, eyex, eydx, dyex, dydxavg, eyexavg, eydxavg, dyexavg, ratio, ratioavg, lnratio, lnratioavg, lnor, lnoravg, expdydx, expdydxavg

See the Transformations section below for definitions of each transformation.

function: accept two equal-length numeric vectors of adjusted predictions (

`hi`

and`lo`

) and returns a vector of contrasts of the same length, or a unique numeric value.See the Transformations section below for examples of valid functions.

- transform_post
(experimental) A function applied to unit-level estimates and confidence intervals just before the function returns results.

- draw
`TRUE`

returns a`ggplot2`

plot.`FALSE`

returns a`data.frame`

of the underlying data.- ...
Additional arguments are passed to the

`predict()`

method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the`marginaleffects`

website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the`?marginaleffects`

documentation for a non-exhaustive list of available arguments.

A `ggplot2`

object