model

Model object

variables

Name of the variable whose marginal effect (slope) we want to plot on the yaxis.

condition

Conditional slopes

Character vector (max length 4): Names of the predictors to display.

Named list (max length 4): 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: xaxis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid).

Numeric variables in positions 2 and 3 are summarized by Tukey’s five numbers ?stats::fivenum .

by

Aggregate unitlevel estimates (aka, marginalize, average over). Valid inputs:

FALSE : return the original unitlevel estimates.

TRUE : aggregate estimates for each term.

Character vector of column names in newdata or in the data frame produced by calling the function without the by argument.

Data frame with a by column of group labels, and merging columns shared by newdata or the data frame produced by calling the same function without the by argument.

See examples below.

For more complex aggregations, you can use the FUN argument of the hypotheses() function. See that function’s documentation and the Hypothesis Test vignettes on the marginaleffects website.

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 slopes() 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 modelspecific list of acceptable values is returned in an error message. When type is NULL , the first entry in the error message is used by default.

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: Unitlevel standard errors using the default vcov(model) variancecovariance matrix.

String which indicates the kind of uncertainty estimates to return.

Heteroskedasticityconsistent: “HC” , “HC0” , “HC1” , “HC2” , “HC3” , “HC4” , “HC4m” , “HC5” . See ?sandwich::vcovHC

Heteroskedasticity and autocorrelation consistent: “HAC”

MixedModels degrees of freedom: "satterthwaite", "kenwardroger"

Other: “NeweyWest” , “KernHAC” , “OPG” . See the sandwich package documentation.

Onesided 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

logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in avg_*() or with the by argument, and not unitlevel estimates. See ?weighted.mean

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).

FALSE: Equal weights.

TRUE: Extract weights from the fitted object with insight::find_weights() and use them when taking weighted averages of estimates. Warning: newdata=datagrid() returns a single average weight, which is equivalent to using wts=FALSE

slope

string indicates the type of slope or (semi)elasticity to compute:

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 mixedeffects 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 "ModelSpecific Arguments" section of the ?slopes documentation for a nonexhaustive list of available arguments.
