This alias is kept for backward compatibility.

## Usage

```
plot_cco(
model,
variables = NULL,
condition = NULL,
by = NULL,
newdata = NULL,
type = "response",
vcov = NULL,
conf_level = 0.95,
wts = NULL,
comparison = "difference",
transform = NULL,
rug = FALSE,
gray = FALSE,
draw = TRUE,
...
)
```

## Arguments

- model
Model object

- condition
Conditional predictions

Character vector (max length 3): Names of the predictors to display.

Named list (max length 3): List names correspond to predictors. List elements can be:

Numeric vector

Function which returns a numeric vector or a set of unique categorical values

Shortcut strings for common reference values: "minmax", "quartile", "threenum"

1: x-axis. 2: color/shape. 3: facets.

Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers

`?stats::fivenum`

- by
Marginal predictions

Character vector (max length 3): Names of the categorical predictors to marginalize across.

1: x-axis. 2: color. 3: facets.

- newdata
When

`newdata`

is`NULL`

, the grid is determined by the`condition`

argument. When`newdata`

is not`NULL`

, the argument behaves in the same way as in the`predictions()`

function.- type
string indicates the type (scale) of the predictions used to compute contrasts or slopes. 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. When

`type`

is`NULL`

, the default value is used. This default is the first model-related row in the`marginaleffects:::type_dictionary`

dataframe.- 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.

- wts
string or numeric: weights to use when computing average contrasts or slopes. These weights only affect the averaging in

`avg_*()`

or with the`by`

argument, and not the unit-level estimates themselves.string: column name of the weights variable in

`newdata`

. When supplying a column name to`wts`

, it is recommended to supply the original data (including the weights variable) explicitly to`newdata`

.numeric: vector of length equal to the number of rows in the original data or in

`newdata`

(if supplied).

- transform
A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries.

- rug
TRUE displays tick marks on the axes to mark the distribution of raw data.

- gray
FALSE grayscale or color plot

- 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.

## Model-Specific Arguments

Some model types allow model-specific arguments to modify the nature of
marginal effects, predictions, marginal means, and contrasts. Please report
other package-specific `predict()`

arguments on Github so we can add them to
the table below.

https://github.com/vincentarelbundock/marginaleffects/issues

Package | Class | Argument | Documentation |

`brms` | `brmsfit` | `ndraws` | brms::posterior_predict |

`re_formula` | brms::posterior_predict | ||

`lme4` | `merMod` | `re.form` | lme4::predict.merMod |

`allow.new.levels` | lme4::predict.merMod | ||

`glmmTMB` | `glmmTMB` | `re.form` | glmmTMB::predict.glmmTMB |

`allow.new.levels` | glmmTMB::predict.glmmTMB | ||

`zitype` | glmmTMB::predict.glmmTMB | ||

`mgcv` | `bam` | `exclude` | mgcv::predict.bam |

`robustlmm` | `rlmerMod` | `re.form` | robustlmm::predict.rlmerMod |

`allow.new.levels` | robustlmm::predict.rlmerMod | ||

`MCMCglmm` | `MCMCglmm` | `ndraws` |

## Examples

```
mod <- lm(mpg ~ hp + wt, data = mtcars)
plot_predictions(mod, condition = "wt")
mod <- lm(mpg ~ hp * wt * am, data = mtcars)
plot_predictions(mod, condition = c("hp", "wt"))
plot_predictions(mod, condition = list("hp", wt = "threenum"))
plot_predictions(mod, condition = list("hp", wt = range))
```