This document describes how to plot estimates as forest plots (or dot
whisker plots) 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. For mixed effects models, only fixed effects
are plotted by default as well.

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

First, we fit a model that will be used in the following examples. The examples work in the same way for any other model as well.

```
# create binary response
y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)
# create data frame for fitting model
df <- data.frame(
y = to_factor(y),
sex = to_factor(efc$c161sex),
dep = to_factor(efc$e42dep),
barthel = efc$barthtot,
education = to_factor(efc$c172code)
)
# set variable label for response
set_label(df$y) <- "High Negative Impact"
# fit model
m1 <- glm(y ~., data = df, family = binomial(link = "logit"))
```

The simplest function call is just passing the model object as argument. By default, estimates are sorted in descending order, with the highest effect at the top.

The “neutral” line, i.e. the vertical intercept that indicates no
effect (x-axis position 1 for most glm’s and position 0 for most linear
models), is drawn slightly thicker than the other grid lines. You can
change the line color with the `vline.color`

-argument.

By default, the estimates are sorted in the same order as they were
introduced into the model. Use `sort.est = TRUE`

to sort
estimates in descending order, from highest to lowest value.

Another way to sort estimates is to use the
`order.terms`

-argument. This is a numeric vector, indicating
the order of estimates in the plot. In the summary, we see that “sex2”
is the first term, followed by the three dependency-categories (position
2-4), the Barthel-Index (5) and two levels for intermediate and high
level of education (6 and 7).

```
summary(m1)
#>
#> Call:
#> glm(formula = y ~ ., family = binomial(link = "logit"), data = df)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.2654 -0.9275 0.4610 0.9464 2.0215
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.700232 0.576715 1.214 0.224682
#> sex2 0.649136 0.186186 3.486 0.000489 ***
#> dep2 0.485259 0.361498 1.342 0.179480
#> dep3 1.125130 0.361977 3.108 0.001882 **
#> dep4 0.910194 0.441774 2.060 0.039368 *
#> barthel -0.029802 0.004732 -6.298 3.02e-10 ***
#> education2 0.226525 0.200298 1.131 0.258081
#> education3 0.283600 0.249327 1.137 0.255346
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1122.16 on 814 degrees of freedom
#> Residual deviance: 939.77 on 807 degrees of freedom
#> (93 observations deleted due to missingness)
#> AIC: 955.77
#>
#> Number of Fisher Scoring iterations: 4
```

Now we want the educational levels (6 and 7) first, than gender (1),
followed by dependency (2-4)and finally the Barthel-Index (5). Use this
order as numeric vector for the `order.terms`

-argument.

By default, `plot_model()`

automatically exponentiates
coefficients, if appropriate (e.g. for models with log or logit link).
You can explicitley prevent transformation by setting the
`transform`

-argument to `NULL`

, or apply any
transformation by using a character vector with the function name.

By default, just the dots and error bars are plotted. Use
`show.values = TRUE`

to show the value labels with the
estimates values, and use `show.p = FALSE`

to suppress the
asterisks that indicate the significance level of the p-values. Use
`value.offset`

to adjust the relative positioning of value
labels to the dots and lines.

As seen in the above examples, by default, the plotting-functions of
**sjPlot** retrieve value and variable labels if the data
is *labelled*, using the sjlabelled-package.
If the data is not labelled, the variable names are used. In such cases,
use the arguments `title`

, `axis.labels`

and
`axis.title`

to annotate the plot title and axes. If you want
variable names instead of labels, even for labelled data, use
`""`

as argument-value, e.g. `axis.labels = ""`

,
or set `auto.label`

to `FALSE`

.

Furthermore, `plot_model()`

applies case-conversion to all
labels by default, using the snakecase-package.
This converts labels into human-readable versions. Use
`case = NULL`

to turn case-conversion off, or refer to the
package-vignette of the **snakecase**-package for further
options.

Use `terms`

resp. `rm.terms`

to select specific
terms that should (not) be plotted.

For linear models, you can also plot standardized beta coefficients,
using `type = "std"`

or `type = "std2"`

. These two
options differ in the way how coefficients are standardized.
`type = "std2"`

plots standardized beta values, however,
standardization follows Gelman’s (2008) suggestion, rescaling the
estimates by dividing them by two standard deviations instead of just
one.

`plot_model()`

also supports stan-models fitted with the
**rstanarm** or **brms** packages. However,
there are a few differences compared to the previous plot examples.

First, of course, there are no *confidence intervals*, but
*uncertainty intervals* - high density intervals, to be
precise.

Second, there’s not just one interval range, but an *inner*
and *outer* probability. By default, the inner probability is
fixed to `.5`

(50%), while the outer probability is specified
via `ci.lvl`

(which defaults to `.89`

(89%) for
Bayesian models). However, you can also use the arguments
`prob.inner`

and `prob.outer`

to define the
intervals boundaries.

Third, the point estimate is by default the *median*, but can
also be another value, like mean. This can be specified with the
`bpe`

-argument.

```
if (require("rstanarm", quietly = TRUE)) {
# make sure we apply a nice theme
library(ggplot2)
theme_set(theme_sjplot())
data(mtcars)
m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)
# default model
plot_model(m)
# same model, with mean point estimate, dot-style for point estimate
# and different inner/outer probabilities of the HDI
plot_model(
m,
bpe = "mean",
bpe.style = "dot",
prob.inner = .4,
prob.outer = .8
)
}
```

There are several options to customize the plot appearance:

- The
`colors`

-argument either takes the name of a valid colorbrewer palette (see also the related vignette),`"bw"`

or`"gs"`

for black/white or greyscaled colors, or a string with a color name. `value.offset`

and`value.size`

adjust the positioning and size of value labels, if shown.`dot.size`

and`line.size`

change the size of dots and error bars.`vline.color`

changes the neutral “intercept” line.`width`

,`alpha`

and`scale`

are passed down to certain ggplot-geoms, like`geom_errorbar()`

or`geom_density_ridges()`

.

Gelman A (2008) *Scaling regression inputs by dividing by two
standard deviations.* Statistics in Medicine 27: 2865–2873.