This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the upper panel in `plot_specs()`

.

```
plot_curve(
df,
var = .data$estimate,
desc = FALSE,
ci = TRUE,
ribbon = FALSE,
legend = FALSE,
null = 0
)
```

- df
a data frame resulting from

`run_specs()`

.- var
which variable should be evaluated? Defaults to estimate (the effect sizes computed by

`run_specs()`

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

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

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

- legend
logical value indicating whether the legend should be plotted Defaults to FALSE.

- null
Indicate what value represents the null hypothesis (Defaults to zero)

a ggplot object.

```
# load additional library
library(ggplot2) # for further customization of the plots
# Run specification curve analysis
results <- run_specs(df = example_data,
y = c("y1", "y2"),
x = c("x1", "x2"),
model = c("lm"),
controls = c("c1", "c2"),
subsets = list(group1 = unique(example_data$group1),
group2 = unique(example_data$group2)))
# Plot simple specification curve
plot_curve(results)
# Ribbon instead of CIs and customize further
plot_curve(results, ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = median(results$estimate),
linetype = "dashed") +
theme_linedraw()
```