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Power curves for a Solomon design

Source: R/solomon_plot_power.R
plot_power_solomon.Rd

Draws power against sample size for each Solomon estimand, so the effect of cell size, effect size, pretest-posttest correlation, and sensitization on a planned study can be seen rather than read from a table. A horizontal line marks the target power, and each curve is annotated with the design plan_solomon() returns for that target.

Usage

plot_power_solomon(
  n = seq(10, 150, by = 10),
  delta = 0.3,
  sens = 0,
  rho = 0.5,
  sigma = 1,
  alpha = 0.05,
  estimand = c("ate", "sensitization", "pretested", "unpretested"),
  allocation = c(1, 1, 1, 1),
  target = 0.8,
  method = c("analytic", "simulation"),
  sims = 500,
  seed = NULL
)

Arguments

n

Sizes of the smallest cell to plot. Other cells follow allocation.

delta, sens, rho

Treatment effect among unpretested participants, sensitization, and pretest-posttest correlation, as in power_solomon(). At most one of them may have more than one value; that one is shown by color.

sigma

Posttest residual standard deviation in all cells.

alpha

Two-sided significance level. Default is 0.05.

estimand

Estimands to plot: any of "ate", "sensitization", "pretested", and "unpretested".

allocation

Relative cell sizes for n1 to n4, as in plan_solomon().

target

Target power marked on the figure. Default is 0.80. Use NULL to omit the target line and the planned designs.

method

"analytic" (default) or "simulation".

sims

Replications per point when method = "simulation".

seed

Optional seed for method = "simulation".

Value

A ggplot object.

Details

Curves use the normal-theory power calculations validated for power_solomon() by default. With method = "simulation", each point is the rejection rate of the package's own GLM test from power_solomon(), shown with a band of two Monte Carlo standard errors.

Power differs sharply between estimands: the sensitization contrast has four times the sampling variance of the average treatment effect, so its curve rises far more slowly. The figure is faceted by estimand to keep that difference visible.

See also

power_solomon(), plan_solomon()

Examples

plot_power_solomon(n = seq(10, 200, by = 10), delta = 0.4, sens = 0.2)

plot_power_solomon(n = seq(10, 200, by = 10), delta = 0.4, rho = c(0, 0.5, 0.8),
                   estimand = c("ate", "pretested"))