Create data and plot power curves calculated using power_marginaleffect() for a list of models

Iterate a process of simulating test data from test_data_fun, making predictions using models in model_list, and calculating power using power_marginaleffect() across a number of sample sizes ns and iterations n_iter. The results are averaged and used to create a plot of the resulting power curves.

Usage

repeat_power_marginaleffect(
  target_effect,
  exposure_prob,
  model_list = default_power_model_list(),
  test_data_fun = function(n) {
     glm_data(Y ~ 1 + 3 * log(W), W = stats::runif(n, min
    = 1, max = 50))
 },
  ns = seq(10, 300, 10),
  desired_power = 0.9,
  n_iter = 1,
  ...
)

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

# S3 method for class 'postcard_rpm'
summary(object, ...)

# S3 method for class 'summary.postcard_rpm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

target_effect

Passed to power_marginaleffect()

exposure_prob

Passed to power_marginaleffect()

model_list

a named list of models used to get predictions on generated test data sets that are then passed to power_marginaleffect() as predictions. The elements of model_list need to have an existing predict() method. The default is an ANCOVA and a prognostic model fitted with fit_best_learner() to a simple data set of 1000 observations generated with a non-linear effect of a single covariate using glm_data().

test_data_fun

a function with a single argument n that generates test data sets for the sample sizes ns specified. The default generates data using glm_data() with the same data generating process as the training data used to fit the default models in model_list.

ns

a numeric vector of sample sizes

desired_power

a numeric between 0 and 1 indicating the desired power level

n_iter

a numeric indicating a number of iterations to process and average over

...

additional arguments passed to power_marginaleffect()

x

an object of class postcard_rpm created by repeat_power_marginaleffect()

cols

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

object

an object of class postcard_rpm created by repeat_power_marginaleffect()

digits

a numeric with the number of digits to display when printing.

Value

repeat_power_marginal returns an object of class postcard_rpm, which is just a data.frame with plot and summary methods defined. The plot method returns a ggplot2 object. The summary method returns an object of class summary.postcard_rpm holding the power assumptions and a data.frame with, for each model, the smallest sample size achieving the desired power (NA if not reached), the power at the largest sample size and the maximum power.

See also

repeat_power_linear() for a similar implementation to iterate the process of approximating power with the functions in power_linear()

Examples

# Note everything is wrapped in dontrun to avoid long runtimes of examples (tests are
# still in place). Reduce the number of sample sizes and/or iterations to avoid long
# runtimes
if (FALSE) { # \dontrun{
# A simple use case with default models and test data (we run only with a few sample
# sizes to reduce runtime of examples)
rpm <- repeat_power_marginaleffect(
  target_effect = 0.9,
  exposure_prob = 0.5
)
plot(rpm)

################################
# Create model from a poisson family and estimate the power of rate ratio with
# several arguments passed to power_marginaleffect
################################
b1 <- 0.9
b2 <- 0.2
b3 <- -0.4
b4 <- -0.6

train_pois <- glm_data(
  Y ~ b1*log(X1)+b2*X2+b3*X3+b4*X2*X3,
  X1 = runif(1e3, min = 1, max = 10),
  X2 = rnorm(1e3),
  X3 = rgamma(1e3, shape = 1),
  family = poisson()
)

# Define models to compare fit to training data
ancova_prog_list <- list(
ANCOVA = glm(Y ~ X1 + X2 + X3, data = train_pois, family = poisson),
"ANCOVA with prognostic score" = fit_best_learner(list(mod = Y ~ X1 + X2 + X3), data = train_pois)
)

# Create a function that produces data to predict on
test_pois_fun <- function(n) {
 glm_data(
   Y ~ b1*log(X1)+b2*X2+b3*X3+b4*X2*X3,
   X1 = runif(n, min = 1, max = 10),
   X2 = rnorm(n),
   X3 = rgamma(n, shape = 1),
   family = poisson()
 )
}

# Specify a bunch of different arguments that are passed to power_marginaleffect()
## Run for 2 sample sizes to reduce runtime
rpm_rr <- repeat_power_marginaleffect(
  model_list = ancova_prog_list,
  test_data_fun = test_pois_fun,
  ns = seq(100, 200), n_iter = 1,
  var1 = function(var0) 1.1 * var0,
  kappa1_squared = function(kap0) 1.1 * kap0,
  estimand_fun = "rate_ratio",
  target_effect = 1.4,
  exposure_prob = 1/2,
  margin = 0.8
)
plot(rpm_rr2)
} # }