Plot Conditional or Marginal Slopes

Description

Plot slopes 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 slopes, that is, slopes made on the original data, but averaged by subgroups. This is analogous to using the by argument in the slopes() function.

The condition argument is used to plot conditional slopes, that is, slopes computed on a user-specified grid. This is analogous to using the newdata argument and datagrid() function in a slopes() call. All variables whose values are not specified explicitly are treated as usual by datagrid(), that is, they are held at their mean or mode (or rounded mean for integers). This includes grouping variables in mixed-effects models, so analysts who fit such models may want to specify the groups of interest using the condition argument, or supply model-specific arguments to compute population-level estimates. See details below. See the "Plots" vignette and website for tutorials and information on how to customize plots:

• https://marginaleffects.com/bonus/plot.html

• https://marginaleffects.com

Usage

plot_slopes(
  model,
  variables = NULL,
  condition = NULL,
  by = NULL,
  newdata = NULL,
  type = NULL,
  vcov = NULL,
  conf_level = 0.95,
  wts = FALSE,
  slope = "dydx",
  rug = FALSE,
  gray = getOption("marginaleffects_plot_gray", default = FALSE),
  draw = TRUE,
  ...
)

Arguments

model	Model object
variables	Name of the variable whose marginal effect (slope) we want to plot on the y-axis.
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: x-axis. 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 unit-level estimates (aka, marginalize, average over). Valid inputs: FALSE: return the original unit-level 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 model-specific 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: 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	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 unit-level 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: "dydx": dY/dX "eyex": dY/dX * Y / X "eydx": dY/dX * Y "dyex": dY/dX / X Y is the predicted value of the outcome; X is the observed value of the predictor.
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 ?slopes documentation for a non-exhaustive list of available arguments.

Value

A ggplot2 object

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
	gam	exclude	mgcv::predict.gam
robustlmm	rlmerMod	re.form	robustlmm::predict.rlmerMod
		allow.new.levels	robustlmm::predict.rlmerMod
MCMCglmm	MCMCglmm	ndraws
sampleSelection	selection	part	sampleSelection::predict.selection

Examples

library("marginaleffects")

library(marginaleffects)
mod <- lm(mpg ~ hp * drat * factor(am), data = mtcars)

plot_slopes(mod, variables = "hp", condition = "drat")

plot_slopes(mod, variables = "hp", condition = c("drat", "am"))

plot_slopes(mod, variables = "hp", condition = list("am", "drat" = 3:5))

plot_slopes(mod, variables = "am", condition = list("hp", "drat" = range))

plot_slopes(mod, variables = "am", condition = list("hp", "drat" = "threenum"))