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).
a data frame resulting from
a vector specifying which analytical choices should be plotted. By default, all choices are plotted.
labels for the two parts of the plot
vector indicating the relative heights of the plot.
logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE.
Indicate what value represents the 'null' hypothesis (defaults to zero).
logical value indicating whether confidence intervals should be plotted.
logical value indicating whether a ribbon instead should be plotted.
additional arguments that can be passed to
a ggplot object.
# 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))