This function plots an entire visualization of the specification curve
analysis. The function uses the entire tibble that is produced by `run_specs()`

to create a standard visualization of the specification curve analysis. Alternatively, one can also pass two separately created ggplot objects to the function. In this case, it simply combines them using `cowplot::plot_grid`

. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant).

- df
a data frame resulting from

`run_specs()`

.- plot_a
a ggplot object resulting from

`plot_curve()`

(or`plot_choices()`

respectively).- plot_b
a ggplot object resulting from

`plot_choices()`

(or`plot_curve()`

respectively).- choices
a vector specifying which analytical choices should be plotted. By default, all choices are plotted.

- labels
labels for the two parts of the plot

- rel_heights
vector indicating the relative heights of the plot.

- desc
logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE.

- null
Indicate what value represents the 'null' hypothesis (defaults to zero).

- ci
logical value indicating whether confidence intervals should be plotted.

- ribbon
logical value indicating whether a ribbon instead should be plotted.

- ...
additional arguments that can be passed to

`plot_grid()`

.

a ggplot object.

`plot_curve()`

to plot only the specification curve.`plot_choices()`

to plot only the choices panel.`plot_samplesizes()`

to plot a histogram of sample sizes per specification.

```
# load additional library
library(ggplot2) # for further customization of the plots
# run spec analysis
results <- run_specs(example_data,
y = c("y1", "y2"),
x = c("x1", "x2"),
model = "lm",
controls = c("c1", "c2"),
subset = list(group1 = unique(example_data$group1)))
# plot results directly
plot_specs(results)
# Customize each part and then combine
p1 <- plot_curve(results) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey") +
ylim(-3, 12) +
labs(x = "", y = "regression coefficient")
p2 <- plot_choices(results) +
labs(x = "specifications (ranked)")
plot_specs(plot_a = p1, # arguments must be called directly!
plot_b = p2,
rel_height = c(2, 2))
```