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 via .`type = "emm"`

, which is similar to`type = "eff"`

. It internally calls via .`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.

`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())
<- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
fit
plot_model(fit, type = "pred", terms = "c12hour")
```

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

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"))`

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

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

:

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

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)
$c172code <- sjlabelled::as_factor(efc$c172code)
efc<- lm(neg_c_7 ~ c12hour + c172code, data = efc)
fit
# 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),
# which is about 1.9715
plot_model(fit, type = "eff", terms = "c12hour")
```

Plotting interaction terms are described in a separate vignette.