# Plotting Marginal Effects of Regression Models" In sjPlot: Data Visualization for Statistics in Social Science

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>",
message = FALSE,
dev = "png",
fig.width = 7,
fig.height = 3.5,
warning = FALSE,
eval = TRUE
# eval = if (isTRUE(exists("params"))) params\$EVAL else FALSE
)
options(width = 800, tibble.width = Inf)
```

This document describes how to plot marginal effects of various regression models, using the `plot_model()` function. `plot_model()` is a generic plot-function, which accepts many model-objects, like `lm`, `glm`, `lme`, `lmerMod` etc.

`plot_model()` allows to create various plot tyes, which can be defined via the `type`-argument. The default is `type = "fe"`, which means that fixed effects (model coefficients) are plotted. To plot marginal effects, call `plot_model()` with:

• `type = "pred"` to plot predicted values (marginal effects) for specific model terms.
• `type = "eff"`, which is similar to `type = "pred"`, however, discrete predictors are held constant at their proportions (not reference level). It internally calls \code{\link[effects]{Effect}} via \code{\link[ggeffects]{ggeffect}}.
• `type = "emm"`, which is similar to `type = "eff"`. It internally calls \code{\link[emmeans]{emmeans}} via \code{\link[ggeffects]{ggemmeans}}.
• `type = "int"` to plot marginal effects of interaction terms.

To plot marginal effects of regression models, at least one model term needs to be specified for which the effects are computed. It is also possible to compute marginal effects for model terms, grouped by the levels of another model's predictor. The function also allows plotting marginal effects for two- or three-way-interactions, however, this is shown in a different vignette.

`plot_model()` supports labelled data and automatically uses variable and value labels to annotate the plot. This works with most regression modelling functions.

Note:* For marginal effects plots, sjPlot calls functions from the ggeffects-package. If you need more flexibility when creating marginal effects plots, consider directly using the ggeffects*-package.

# Marginal effects

`plot_model(type = "pred")` computes predicted values for all possible levels and values from a model's predictors. In the simplest case, a fitted model is passed as first argument, followed by the `type` argument and the term in question as `terms` argument:

```library(sjPlot)
library(ggplot2)
data(efc)
theme_set(theme_sjplot())

fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

plot_model(fit, type = "pred", terms = "c12hour")
```

The plot shows the predicted values for the response at each value from the term c12hour.

## Marginal effects for different groups

The `terms`-argument accepts up to three model terms, where the second and third term indicate grouping levels. This allows predictions for the term in question at different levels for other model terms:

```plot_model(fit, type = "pred", terms = c("c12hour", "c172code"))
```

A second grouping structure can be defined, which will create a plot with multiple panels in grid layout:

```plot_model(fit, type = "pred", terms = c("c12hour", "c172code", "c161sex"))
```

## Marginal effects at specific values or levels

The `terms`-argument not only defines the model terms of interest, but each model term that defines the grouping structure can be limited to certain values. This allows to compute and plot marginal effects for terms at specific values only. To define these values, put them in square brackets directly after the term name: `terms = c("c12hour [30, 50, 80]", "c172code [1,3]")`

```plot_model(fit, type = "pred", terms = c("c12hour [30, 50, 80]", "c172code [1,3]"))
```

Note that in the above plot, although the values 30, 50 and 80 only are selected from c12hour, the continuous scale automatically adds panel grids every 5 units along the x-axis.

Defining own values is especially useful when variables are, for instance, log-transformed. `plot_model()` then typically only uses the range of the log-transformed variable, which is in most cases not what we want. In such situation, specify the range in the `terms`-argument.

```data(mtcars)
mpg_model <- lm(mpg ~ log(hp), data = mtcars)

# x-values and predictions based on the log(hp)-values
plot_model(mpg_model, type = "pred", terms = "hp")

# x-values and predictions based on hp-values from 50 to 150
plot_model(mpg_model, type = "pred", terms = "hp [50:150]")
```

The brackets in the `terms`-argument also accept the name of a valid function, to (back-)transform predicted valued. In this example, an alternative would be to specify that values should be exponentiated, which is indicated by `[exp]` in the `terms`-argument:

```# x-values and predictions based on exponentiated hp-values
plot_model(mpg_model, type = "pred", terms = "hp [exp]")
```

## Polynomial terms and splines

The function also works for models with polynomial terms or splines. Following code reproduces the plot from `?splines::bs`:

```library(splines)
data(women)

fm1 <- lm(weight ~ bs(height, df = 5), data = women)
plot_model(fm1, type = "pred", terms = "height")
```

## Different constant values for factors

Model predictions are based on all possible combinations of the model terms, which are - roughly speaking - created using `expand.grid()`. For the terms in question, all values are used for combinations. All other model predictors that are not specified in the `terms`-argument, are held constant (which is achieved with `sjstats::typical_value()`). By default, continuous variables are set to their mean, while factors are set to their reference level.

```data(efc)
efc\$c172code <- sjlabelled::as_factor(efc\$c172code)
fit <- lm(neg_c_7 ~ c12hour + c172code, data = efc)

# reference category is used for "c172code", i.e. c172code
# used the first level as value for predictions
plot_model(fit, type = "pred", terms = "c12hour")
```

However, one may want to set factors to their proportions instead of reference level. E.g., a factor gender with value 0 for female and value 1 for male persons, would be set to `0` when marginal effects are computed with `type = "pred"`. But if 40% of the sample are female persons, another possibility to hold this factor constant is to use the value `.4` (reflecting the proportion of 40%). If this is required, use `type = "eff"`, which internally does not call `predict()` to compute marginal effects, but rather `effects::effect()`.

```# proportion is used for "c172code", i.e. it is set to
# mean(sjlabelled::as_numeric(efc\$c172code), na.rm = T),
plot_model(fit, type = "eff", terms = "c12hour")
```

# Interaction terms

Plotting interaction terms are described in a separate vignette.

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sjPlot documentation built on July 10, 2021, 5:07 p.m.