as.data.frame = TRUE, ...)A data.frame over which to calculate marginal effects. parent.frame()), variables = NULL, type = c("response", "link"), This is where marginal effects come in handy. variables = NULL, type = c("response", "link"), eps = 1e-07, marginal_effects(model, data = find_data(model, parent.frame()), marginal_effects(model, data, variables = NULL, ...)# S3 method for default marginal_effects(model, data = find_data(model, eps = 1e-07, as.data.frame = TRUE, varslist = NULL, ...)# S3 method for glm parent.frame()), variables = NULL, eps = 1e-07, varslist = NULL, marginal_effects(model, data = find_data(model, Mostly relevant for non-linear models, where the reasonable options are “response” (the default) or “link” (i.e., on the scale of the linear predictor in a GLM).A numeric value specifying the “step” to use when calculating numerical derivatives. marginal_effects(model, data = find_data(model), This is optional, but may be required when the underlying modelling function sets A character vector with the names of variables for which to compute the marginal effects. 8 This simple addition to the standard marginal effects plot should discourage researchers from inferring that interaction effects exist when they do not. It is also possible to compute marginal effects for model terms, grouped by the levels of another model’s predictor. parent.frame()), variables = NULL, eps = 1e-07, varslist = NULL, The default (A character string indicating the type of marginal effects to estimate. Following code reproduces the plot from Model predictions are based on all possible combinations of the model terms, which are - roughly speaking - created using # x-values and predictions based on the log(hp)-values# x-values and predictions based on hp-values from 50 to 150# x-values and predictions based on exponentiated hp-values# reference category is used for "c172code", i.e.
More specifically, you could use the package ggeffects to visualize the marginal effects of key variables. marginal_effects(model, data = find_data(model, it is set to# mean(sjlabelled::as_numeric(efc$c172code), na.rm = T), This is an S3 generic method for calculating the marginal effects of … parent.frame()), variables = NULL, type = c("response", "link"), as.data.frame = TRUE, varslist = NULL, ...)# S3 method for loess By default this is the smallest floating point value that can be represented on the present architecture.A logical indicating whether to return a data frame (the default) or a matrix.Methods are currently implemented for the following object classes:A method is also provided for the object classes “margins” to return a simplified data frame from complete “margins” objects. The margins and prediction packages are a combined effort to port the functionality of Stata’s (closed source) margins command to (open source) R. The major functionality of margins - namely the estimation of marginal (or partial) effects - is provided through a single function, margins(). To plot marginal effects of regression models, at least one model term needs to be specified for which the effects are computed.
The function also allows plotting marginal effects for two- or three-way-interactions, however, this is shown in a different vignette. variables = NULL, type = c("response", "link"), eps = 1e-07, It is also possible to compute marginal effects for model terms, grouped by the levels of another model’s predictor. as.data.frame = TRUE, ...)# S3 method for polr c172code# proportion is used for "c172code", i.e.

They enable you to show how a change of the independent variable of interest impacts your dependent variable, while taking all other independent variables into account. I provide open source software in R to create figures similar to Figure 8 in the R package interplot.medline, which is based on the interplot package in R by Solt and Hu (2016).