Package 'tout'

Plot sampling distributions of three-outcome designs

Description

Takes an object of class tout and plots sampling distributions under the null and alternative hypotheses, highlighting which portions correspond to stop, pause, and go outcomes.

Usage

## S3 method for class 'tout'
plot(x, ...)

Arguments

x	object of class tout as produced by ⁠tout_design().⁠
...	further arguments passed to or from other methods.

Value

no return value, called for side effects.

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Print a tout object

Description

The default print method for a tout object.

Usage

## S3 method for class 'tout'
print(x, ...)

Arguments

x	object of class tout as produced by tout_design().
...	further arguments passed to or from other methods.

Value

no return value, called for side effects.

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Find optimal sample size and progression criteria

Description

Given a null and alternative hypothesis, this function finds the lowest sample size such that a design with optimal progression criteria (as determined by the function opt_pc) satisfies upper constraints on three operating characteristics.

Usage

tout_design(
  rho_0,
  rho_1,
  alpha_nom,
  beta_nom,
  gamma_nom = 1,
  eta_0 = 0.5,
  eta_1 = eta_0,
  tau = c(0, 0),
  max_n = NULL,
  n = NULL,
  x = NULL,
  sigma = NULL
)

Arguments

rho_0	null hypothesis.
rho_1	alternative hypothesis.
alpha_nom	nominal upper constraint on alpha.
beta_nom	nominal upper constraint on beta.
gamma_nom	nominal upper constraint on gamma. Defaults to 1.
eta_0	probability of an incorrect decision under the null hypothesis after an intermediate result. Defaults to 0.5.
eta_1	probability of an incorrect decision under the alternative hypothesis after an intermediate result. Defaults to eta_0.
tau	two element vector denoting lower and upper limits of the effect of adjustment.
max_n	optional upper limit to use in search over sample sizes.
n	optional sample size (optimised if left unspecified).
x	optional vector of decision thresholds (optimised if left unspecified).
sigma	standard deviation of outcome. If left unspecified, a binary outcome is assumed.

Value

An object of class tout, which is a list containing the following components:

valid	boolean indicating if the nominal constraints are met.
n	sample size.
thesholds	numeric vector of the two decision thresholds.
alpha	attained value of operating characteristic alpha.
beta	attained value of operating characteristic beta.
gamma	attained value of operating characteristic gamma.

Examples

rho_0 <- 0.5
rho_1 <- 0.7
alpha_nom <- 0.05
beta_nom <- 0.2

tout_design(rho_0, rho_1, alpha_nom, beta_nom)

# Allowing for adjustment effects:

tout_design(rho_0, rho_1, alpha_nom, beta_nom, tau = c(0.08, 0.12))

# Allowing for different error probabilities following a pause decision

tout_design(rho_0, rho_1, alpha_nom, beta_nom, eta_0 = 0.3)

# Designs for continuous outcomes:

tout_design(rho_0 = 0, rho_1 = 0.4, alpha_nom, beta_nom, sigma = 1)

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