plot_slopes  R Documentation 
Plot slopes on the yaxis against values of one or more predictors (xaxis, 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 userspecified 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 mixedeffects models, so analysts who fit such models may want to specify the groups of interest using the condition
argument, or supply modelspecific arguments to compute populationlevel estimates. See details below.
See the "Plots" vignette and website for tutorials and information on how to customize plots:
https://marginaleffects.com/vignettes/plot.html
https://marginaleffects.com
plot_slopes(
model,
variables = NULL,
condition = NULL,
by = NULL,
newdata = NULL,
type = "response",
vcov = NULL,
conf_level = 0.95,
wts = FALSE,
slope = "dydx",
rug = FALSE,
gray = FALSE,
draw = TRUE,
...
)
model 
Model object 
variables 
Name of the variable whose marginal effect (slope) we want to plot on the yaxis. 
condition 
Conditional slopes

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

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

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

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 

... 
Additional arguments are passed to the 
A ggplot2
object
Some model types allow modelspecific arguments to modify the nature of
marginal effects, predictions, marginal means, and contrasts. Please report
other packagespecific 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  
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"))
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