Generic plot function for 'MADE' objects

Create a faceted plot displaying the results of a set of power analyses. This is a generic function to make facet_grid plots, with specific methods defined for power_MADE, mdes_MADE, and min_studies_MADE objects.

Usage

plot_MADE(
  data,
  v_lines,
  legend_position,
  color,
  numbers,
  number_size,
  numbers_ynudge,
  caption,
  x_lab,
  x_breaks,
  x_limits,
  y_breaks,
  y_limits,
  y_expand = NULL,
  warning,
  traffic_light_assumptions,
  ...
)

Arguments

data: Data/object for which the plot should be made.
v_lines: Integer or vector to specify vertical line(s) in within each plot. Default is NULL.
legend_position: Character string to specify position of legend. Default is "bottom".
color: Logical indicating whether to use color in the plot(s). Default is TRUE.
numbers: Logical indicating whether to number the plots. Default is TRUE.
number_size: Integer value specifying the size of the (optional) plot numbers. Default is 2.5.
numbers_ynudge: Integer value for vertical nudge of the (optional) plot numbers.
caption: Logical indicating whether to include a caption with detailed information regarding the analysis. Default is TRUE.
x_lab: Title for the x-axis. If NULL (the default), the x_lab is specified automatically.
x_breaks: Optional vector to specify breaks on the x-axis. Default is NULL.
x_limits: Optional vector of length 2 to specify the limits of the x-axis. Default is NULL, which allows limits to be determined automatically from the data.
y_breaks: Optional vector to specify breaks on the y-axis.
y_limits: Optional vector of length 2 to specify the limits of the y-axis.
y_expand: Optional vector to expand the limits of the y-axis. Default is NULL.
warning: Logical indicating whether warnings should be returned when multiple models appear in the data. Default is TRUE.
traffic_light_assumptions: Optional logical to specify coloring of strips of the facet grids to emphasize assumptions about the likelihood the given analytical scenario. See Vembye, Pustejovsky, & Pigott (In preparation) for further details.
...: Additional arguments available for some classes of objects.

Value

A ggplot object

References

Vembye, M. H., Pustejovsky, J. E., & Pigott, T. D. (In preparation). Conducting power analysis for meta-analysis of dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Examples

power_dat <-
  power_MADE(
    J = c(50, 56),
    mu = 0.15,
    tau = 0.1,
    omega = 0.05,
    rho = 0,
    sigma2_dist = 4 / 200,
    n_ES_dist = 6
  )
#> Warning: Notice: It is generally recommended not to draw on balanced assumptions regarding the study precision (sigma2js) or the number of effect sizes per study (kjs). See Figures 2A and 2B in Vembye, Pustejovsky, and Pigott (2022).

power_example <-
  plot_MADE(
   data = power_dat,
   power_min = 0.8,
   expected_studies = c(52, 54),
   warning = FALSE,
   caption = TRUE,
   color = TRUE,
   model_comparison = FALSE,
   numbers = FALSE
   )

power_example