Plot function for a 'min_studies' object

Creates a faceted plot with analyses of the minimum number of studies needed to obtained a given effect size with specified levels of power, as calculated using min_studies_MADE.

Usage

# S3 method for min_studies
plot_MADE(
  data,
  v_lines = NULL,
  legend_position = "bottom",
  color = TRUE,
  numbers = TRUE,
  number_size = 2.5,
  numbers_ynudge = NULL,
  caption = TRUE,
  x_lab = NULL,
  x_breaks = NULL,
  x_limits = NULL,
  y_breaks = ggplot2::waiver(),
  y_limits = NULL,
  y_expand = NULL,
  warning = TRUE,
  traffic_light_assumptions = NULL,
  traffic_light_palette = "green-yellow-red",
  v_shade = NULL,
  h_lines = NULL,
  ...
)

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 (forthcoming) for further details.
traffic_light_palette: Character string or character vector to control the color of traffic light strips. If set to 'green-yellow-red' (the default), expected scenarios will be colored green, likely scenarios will be colored yellow, and unlikely scenarios will be colored red. If set to 'greyscale', a gray-scale version of the traffic light plot is provided with white indicating the expected scenario, light gray indicating other plausible scenarios, and dark gray indicating less likely scenarios. Users can also specify their own palette by setting traffic_light_palette to a character vector with colors for the expected, likely, and unlikely scenarios. In this case, the vector must have three entries named 'expected', 'likely', and 'unlikely',
v_shade: Optional vector of length 2 specifying the range of the x-axis interval to be shaded in each plot.
h_lines: Optional integer or vector specifying horizontal lines on each plot.
...: Additional arguments available for some classes of objects.

Value

A ggplot plot showing the minimum number of studies needed to obtain a given effect size with a certain amount of power and level-alpha, faceted across levels of the within-study SD and the between-study SD, with different colors, lines, and shapes corresponding to different values of the assumed sample correlation. If length(unique(data$mu)) > 1, it returns a ggplot plot showing the minimum studies needed to obtained a given effect size with a certain amount of power and level-alpha across effect sizes of practical concern, faceted by the between-study and within-study SDs, with different colors, lines, and shapes corresponding to different values of the assumed sample correlation.

Details

In general, it can be rather difficult to guess/approximate the true model parameters and sample characteristics a priori. Calculating the minimum number of studies needed under just a single set of assumptions can easily be misleading even if the true model and data structure only slightly diverge from the yielded data and model assumptions. To maximize the informativeness of the analysis, Vembye, Pustejovsky, & Pigott (In preparation) suggest accommodating the uncertainty of the power approximations by reporting or plotting power estimates across a range of possible scenarios, which can be done using plot_MADE.power.

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

min_studies_MADE(
  mu = c(0.25, 0.35),
  tau = 0.05,
  omega = 0.02,
  rho = 0.2,
  target_power = .7,
  sigma2_dist = 4 / 200,
  n_ES_dist = 6,
  seed = 10052510
) |>
  plot_MADE(y_breaks = seq(0, 10, 2), numbers = FALSE)
#> 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).