plot_MADE.min_studies {POMADE}

Plot function for a 'min_studies' object

Description

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 class '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,
  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 (In preparation) for further details.

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.

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.

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.

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.

See Also

plot_MADE

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)