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, `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 `add.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)
```

For three grouping variable (i.e. if `terms`

is of length four), one plot per `panel`

(the values of the fourth variable in `terms`

) is created, and a single, integrated plot is produced by default. Use `one.plot = FALSE`

to return one plot per panel.

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.

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.

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.

`ggpredict()`

also supports `coxph`

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

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

).

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.

```
data("lung", package = "survival")
# remove category 3 (outlier, not nice in the plot)
lung <- subset(lung, subset = ph.ecog %in% 0:2)
lung$sex <- factor(lung$sex, labels = c("male", "female"))
lung$ph.ecog <- factor(lung$ph.ecog, labels = c("good", "ok", "limited"))
m <- survival::coxph(survival::Surv(time, status) ~ sex + age + ph.ecog, data = lung)
# predicted risk-scores
pr <- ggpredict(m, c("sex", "ph.ecog"))
plot(pr)
```

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: