# Introduction: Plotting Adjusted Predictions and Marginal Effects

#### Daniel Lüdecke

#### 2023-09-27

Source:`vignettes/introduction_plotmethod.Rmd`

`introduction_plotmethod.Rmd`

## plot()-method

This vignettes demonstrates the `plot()`

-method of the
**ggeffects**-package. It is recommended to read the general introduction first, if you haven’t
done this yet.

If you don’t want to write your own ggplot-code,
**ggeffects** has a `plot()`

-method with some
convenient defaults, which allows quickly creating ggplot-objects.
`plot()`

has some arguments to tweak the plot-appearance. For
instance, `show_ci`

allows you to show or hide confidence
bands (or error bars, for discrete variables), `facets`

allows you to create facets even for just one grouping variable, or
`colors`

allows you to quickly choose from some
color-palettes, including black & white colored plots. Use
`show_data`

to add the raw data points to the plot.

**ggeffects** supports labelled data and
the `plot()`

-method automatically sets titles, axis - and
legend-labels depending on the value and variable labels of the
data.

```
library(ggeffects)
library(sjmisc)
data(efc)
efc$c172code <- to_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
```

### No Facets, in Black & White

```
# don't use facets, b/w figure, w/o confidence bands
plot(dat, colors = "bw", show_ci = FALSE)
```

### Connect Discrete Data Points with Lines

```
# point-geoms for discrete x-axis can be connected with lines
plot(dat, connect_lines = TRUE)
```

## Change appearance of confidence bands

In some plots, the the confidence bands are not represented by a
shaded area (ribbons), but rather by error bars (with line), dashed or
dotted lines. Use `ci_style = "errorbar"`

,
`ci_style = "dash"`

or `ci_style = "dot"`

to
change the style of confidence bands.

## Log-transform y-axis for binomial models

For binomial models, the y-axis indicates the predicted probabilities of an event. In this case, error bars are not symmetrical.

```
library("lme4")
m <- glm(
cbind(incidence, size - incidence) ~ period,
family = binomial,
data = lme4::cbpp
)
dat <- ggpredict(m, "period")
# normal plot, asymmetrical error bars
plot(dat)
```

Here you can use `log_y`

to log-transform the y-axis. The
`plot()`

-method will automatically choose axis breaks and
limits that fit well to the value range and log-scale.

```
# plot with log-transformed y-axis
plot(dat, log_y = TRUE)
```

## Control y-axis appearance

Furthermore, arguments in `...`

are passed down to
`ggplot::scale_y_continuous()`

(resp.
`ggplot::scale_y_log10()`

, if `log_y = TRUE`

), so
you can control the appearance of the y-axis.

## Survival models

`ggpredict()`

also supports `coxph`

-models from
the **survival**-package and is able to either plot
risk-scores (the default), probabilities of survival
(`type = "survival"`

) or cumulative hazards
(`type = "cumulative_hazard"`

).

Since probabilities of survival and cumulative hazards are changing
across time, the time-variable is automatically used as x-axis in such
cases, so the `terms`

-argument only needs up to two
variables.

## Custom color palettes

The **ggeffects**-package has a few pre-defined
color-palettes that can be used with the `colors`

-argument.
Use `show_pals()`

to see all available palettes.

Here are two examples showing how to use pre-defined colors: