adjust_lambda: Adjust Lambda

Description

Finds the value for lambda such that the family wise error rate is protected at level alpha.

Usage

adjust_lambda(design, ...)
# S4 method for OneStageBasket
adjust_lambda(
  design,
  alpha = 0.025,
  p1 = NULL,
  n,
  weight_fun,
  weight_params = list(),
  globalweight_fun = NULL,
  globalweight_params = list(),
  prec_digits,
  ...
)
# S4 method for TwoStageBasket
adjust_lambda(
  design,
  alpha = 0.025,
  p1 = NULL,
  n,
  n1,
  interim_fun,
  interim_params = list(),
  weight_fun,
  weight_params = list(),
  globalweight_fun = NULL,
  globalweight_params = list(),
  prec_digits,
  ...
)

Value

The greatest value with prec_digits decimal places for lambda which controls the family wise error rate at level alpha (one-sided) and the exact family wise error rate for this value of lambda.

Arguments

design: An object of class Basket created by setupOneStageBasket or setupTwoStageBasket.
...: Further arguments.
alpha: The one-sided signifance level.
p1: Probabilities under the alternative hypothesis. If length(p1) == 1, then this is a common probability for all baskets. If is.null(p1) then the type 1 error rate under the global null hypothesis is computed.
n: The sample size per basket.
weight_fun: Which function should be used to calculate the pairwise weights.
weight_params: A list of tuning parameters specific to weight_fun.
globalweight_fun: Which function should be used to calculate the global weights.
globalweight_params: A list of tuning parameters specific to globalweight_fun.
prec_digits: Number of decimal places that are considered when adjusting lambda.
n1: The sample size per basket for the interim analysis in case of a two-stage design.
interim_fun: Which type of interim analysis should be conducted in case of a two-stage design.
interim_params: A list of tuning parameters specific to interim_fun.

Methods (by class)

adjust_lambda(OneStageBasket): Adjust lambda for a single-stage design.
adjust_lambda(TwoStageBasket): Adjust lambda for a two-stage design.

Details

adjust_alpha finds the greatest value with prec_digits for lambda which controls the family wise error rate at level alpha (one-sided). A combination of the uniroot function followed by a grid search is used to finde the correct value for lambda.

Examples

Run this code

design <- setupOneStageBasket(k = 3, shape1 = 1, shape2 = 1, p0 = 0.2)
adjust_lambda(design = design, alpha = 0.025, n = 15,
  weight_fun = weights_fujikawa, prec_digits = 4)

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