postcard (version 1.1.0)

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

Description

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 postcard_rpm
plot(x, cols = NULL, ...)

Value

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

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

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) {
# 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)
}