power_vs_n_plot {MRTSampleSizeBinary}

Returns a plot of power vs sample size in the context of a binary outcome MRT. See the vignette for more details.

Description

Returns a plot of power vs sample size in the context of a binary outcome MRT. See the vignette for more details.

Usage

power_vs_n_plot(
  avail_pattern,
  f_t,
  g_t,
  beta,
  alpha,
  p_t,
  gamma,
  min_n = max(min_samp(alpha, beta), 11),
  max_n = max_samp(min_n)
)

Arguments

avail_pattern

A vector of length T that is the average availability at each time point

f_t

Defines marginal excursion effect MEE(t) under alternative together with beta. Assumed to be matrix of size T*p.

g_t

Defines success probability null curve together with alpha. Assumed to be matrix of size T*q.

beta

Length p vector that defines marginal excursion effect MEE(t) under alternative together with f_t.

alpha

Length q vector that defines success probability null curve together with g_t.

p_t

Length T vector of Randomization probabilities at each time point.

gamma

Desired Type I error

min_n

Minimum of range of sample sizes to plot. Should be greater than the sum of the dimensions of alpha and beta.

max_n

Maximum of range of sample sizes to plot. Should be greater than min_n.

Value

Plot of power and sample size

Examples

           power_vs_n_plot(tau_t_1, f_t_1, g_t_1, beta_1, alpha_1,
                        p_t_1, 0.05, 15, 700)