Plot predictions on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets).

The by argument is used to plot marginal predictions, that is, predictions made on the original data, but averaged by subgroups. This is analogous to using the by argument in the predictions() function.

The condition argument is used to plot conditional predictions, that is, predictions made on a user-specified grid. This is analogous to using the newdata argument and datagrid() function in a predictions() call. Unspecified variables are held at their mean or mode.

See the "Plots" vignette and website for tutorials and information on how to customize plots:

• https://vincentarelbundock.github.io/marginaleffects/articles/plot.html

• https://vincentarelbundock.github.io/marginaleffects

## Usage

plot_predictions(
model,
condition = NULL,
by = NULL,
type = NULL,
vcov = NULL,
conf_level = 0.95,
transform = NULL,
points = 0,
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.

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.

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.

points

Number between 0 and 1 which controls the transparency of raw data points. 0 (default) does not display any points.

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.

## Value

A ggplot2 object or data frame (if draw=FALSE)

## 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))