print.power_report {graphicalMCP}

S3 print method for the class power_report

Description

A printed power_report displays the initial graph, testing and simulation options, power outputs, and optional detailed simulations and test results.

Usage

## S3 method for class 'power_report'
print(x, ..., precision = 4, indent = 2, rows = 10)

Arguments

x

An object of the class power_report to print

...

Other values passed on to other methods (currently unused)

precision

An integer scalar indicating the number of decimal places to to display.

indent

An integer scalar indicating how many spaces to indent results.

rows

An integer scalar indicating how many rows of detailed test results to print.

Value

An object x of the class power_report, after printing the report of conducting power simulations based on a graphical multiple comparison procedure.

References

Bretz, F., Posch, M., Glimm, E., Klinglmueller, F., Maurer, W., and Rohmeyer, K. (2011a). Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests. Biometrical Journal, 53(6), 894-913.

Bretz, F., Maurer, W., and Hommel, G. (2011b). Test and power considerations for multiple endpoint analyses using sequentially rejective graphical procedures. Statistics in Medicine, 30(13), 1489-1501.

Examples

# A graphical multiple comparison procedure with two primary hypotheses (H1
# and H2) and two secondary hypotheses (H3 and H4)
# See Figure 4 in Bretz et al. (2011).
alpha <- 0.025
hypotheses <- c(0.5, 0.5, 0, 0)
delta <- 0.5
transitions <- rbind(
  c(0, delta, 1 - delta, 0),
  c(delta, 0, 0, 1 - delta),
  c(0, 1, 0, 0),
  c(1, 0, 0, 0)
)
g <- graph_create(hypotheses, transitions)

marginal_power <- c(0.8, 0.8, 0.7, 0.9)
corr1 <- matrix(0.5, nrow = 2, ncol = 2)
diag(corr1) <- 1
corr <- rbind(
  cbind(corr1, 0.5 * corr1),
  cbind(0.5 * corr1, corr1)
)
success_fns <- list(
  # Probability to reject both H1 and H2
  `H1andH2` = function(x) x[1] & x[2],
  # Probability to reject both (H1 and H3) or (H2 and H4)
  `(H1andH3)or(H2andH4)` = function(x) (x[1] & x[3]) | (x[2] & x[4])
)
set.seed(1234)
# Bonferroni tests
power_output <- graph_calculate_power(
  g,
  alpha,
  sim_corr = corr,
  sim_n = 1e5,
  power_marginal = marginal_power,
  sim_success = success_fns
)