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Create data and plot power curves calculated using functions in power_linear() for a list of formulas/models

Source: R/repeat_power_plot.R
repeat_power_linear.Rd

Estimate a variance for power approximation using variance_ancova() for each formula in formula_list on train_data. Then calculate power using the function with name specified in power_fun across a number of sample sizes ns for an assumed average treatment effect of ate.

Usage

repeat_power_linear(
  ate,
  formula_list,
  train_data,
  power_fun = c("power_gs", "power_nc"),
  ns = 10:400,
  desired_power = 0.9,
  ...
)

# S3 method for class 'postcard_rpl'
plot(x, cols = NULL, ...)

Arguments

ate

Passed to power_gs() or power_nc()

formula_list

a named list of formulas that are element wise passed to variance_ancova()

train_data

Passed as the data argument in variance_ancova()

power_fun

a character string with value "power_gs" or "power_nc", specifying what function in the power_linear() topic to use

ns

a numeric vector of sample sizes

desired_power

a numeric between 0 and 1 indicating the desired power level

...

Arguments passed to variance_ancova() and power_gs() or power_nc()

x

an object of class postcard_rpl created by repeat_power_linear()

cols

a (potentially named) character vector of colors for the different models in formula_list

Value

repeat_power_linear returns an object of class postcard_rpl, which is just a data.frame with a plot method defined. The plot method returns a ggplot2 object.

See also

repeat_power_marginaleffect() for a similar implementation to iterate the process of approximating power with the power_marginaleffect()

Examples

train_data <- glm_data(
  Y ~ 1+1.5*log(W)+2*X,
  W = runif(1e3, min = 1, max = 10),
  X = rnorm(1e3, sd = 3)
)
rpl <- repeat_power_linear(
  ate = 0.5,
  formula_list = list("ANCOVA 1 covariate" = Y ~ X, "ANCOVA 2 covariates" = Y ~ W + X),
  train_data = train_data)

rpl_nc <- repeat_power_linear(
  ate = 0.5,
  formula_list = list("ANCOVA 1 covariate" = Y ~ X, "ANCOVA 2 covariates" = Y ~ W + X),
  train_data = train_data,
  power_fun = "power_nc",
  df = 1e3-3,
  deflation = 0.95,
  margin = -0.2,
  r = 2)

if (FALSE) { # \dontrun{
plot(rpl)

plot(rpl_nc)
} # }