plot_specs: Plot specification curve and analytical choices
In masurp/specr: Conducting and Visualizing Specification Curve Analyses

plot_specs

R Documentation

Plot specification curve and analytical choices

Description

This function is deprecated because the new version of specr uses a new analytic framework. In this framework, you can plot a similar figure simply by using the generic plot() function and adding the argument type = "default".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).

Usage

plot_specs(
  df = NULL,
  plot_a = NULL,
  plot_b = NULL,
  choices = c("x", "y", "model", "controls", "subsets"),
  labels = c("A", "B"),
  rel_heights = c(2, 3),
  desc = FALSE,
  null = 0,
  ci = TRUE,
  ribbon = FALSE,
  ...
)

Arguments

`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()`.

Value

a ggplot object.

Examples

# 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))

masurp/specr documentation built on March 17, 2024, 5:23 p.m.