Example 7: Two-arm | Fixed design | Single continuous endpoint | Bayesian Go/No-Go with placebo borrowing
example-7.Rmd
# Core simulation framework
library(rxsim)
# Bayesian borrowing utilities
library(RBesT)
#> This is RBesT version 1.8.2 (released 2025-04-25, git-sha b9dab00)
# Helpers
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
set.seed(777)Early development trials often face small sample sizes where borrowing information from historical placebo data can increase efficiency and reduce required sample size. This example demonstrates a Bayesian Go/No-Go decision rule for a Phase IIa trial, where the placebo posterior is informed by historical data via a robust mixture prior, and the treatment posterior uses a non-informative prior. A “Go” decision is issued when the posterior probability of a meaningful treatment effect (Δ > δ) exceeds a predefined threshold γ.
Scenario
A two-arm, fixed design trial (placebo vs treatment) with a single continuous endpoint and a single final analysis.
- Placebo arm uses historical borrowing through an informative prior derived from historical placebo data and robustified with a non-informative component.
- Treatment arm uses a non-informative prior.
- Go/No-Go rule at final analysis:
where , with and .
delta = 0.1 defines the minimum treatment-placebo
difference of clinical relevance — the effect must exceed this threshold
for the drug to be worth advancing. gamma = 0.8 requires
80% posterior probability of exceeding that threshold before a Go
decision is issued, balancing false-positive risk against the cost of a
missed opportunity. The true simulated effect
mu_treat = 0.30 lies above delta, so a
well-powered trial should frequently result in a Go.
# Trial design
sample_size <- 80
allocation <- c(1, 1)
arms <- c("placebo", "treatment")
# Data-generating truth for this simulation example
mu_placebo_true <- 0.00
delta_true <- 0.30 # true treatment - placebo mean difference
sigma_known <- 1.0
# Enrollment/dropout profile (fixed design timeline)
enrollment <- list(
end_time = c(4, 8),
rate = c(6, 10)
)
dropout <- list(
end_time = c(4, 8),
rate = c(0, 0)
)
# Bayesian decision thresholds
delta <- 0.1
gamma <- 0.8
scenario <- tidyr::expand_grid(
sample_size = sample_size,
allocation = list(allocation),
delta_true = delta_true,
delta = delta,
gamma = gamma
)Historical placebo dataset and priors
We include a small historical placebo dataset as study-level means and sample sizes.
Studies H1, H2, and H3 contribute a combined 62 historical placebo
observations and are summarised into an informative prior via
postmix() — a Bayesian update of the non-informative
starting prior with the pooled historical data. robustify()
blends this informative component with a vague component (20% weight),
producing a mixture prior that protects against prior-data conflict: if
the current trial’s placebo behaves unexpectedly, the vague component
limits how strongly the historical data pulls the posterior. The
treatment arm uses prior_noninf (effectively a flat prior),
so the treatment posterior is driven entirely by the current data.
hist_placebo <- data.frame(
study = c("H1", "H2", "H3"),
n = c(24, 18, 20),
mean = c(-0.05, 0.02, 0.00)
)
hist_placebo
#> study n mean
#> 1 H1 24 -0.05
#> 2 H2 18 0.02
#> 3 H3 20 0.00
# Pooled historical summary used to derive an informative placebo prior
n_hist_total <- sum(hist_placebo$n)
m_hist_pooled <- weighted.mean(hist_placebo$mean, hist_placebo$n)
# Non-informative prior (very small pseudo-sample size in mn parametrization)
# Used as treatment prior and as base for historical update.
prior_noninf <- mixnorm(
c(1, 0, 1e-6),
sigma = sigma_known,
param = "mn"
)
# Informative placebo prior from historical placebo summary
prior_placebo_inf <- postmix(
prior_noninf,
n = n_hist_total,
m = m_hist_pooled
)
#> Using default prior reference scale 1
# Robustified placebo prior to protect against prior-data conflict
prior_placebo <- robustify(
prior_placebo_inf,
weight = 0.2,
mean = 0,
n = 1,
sigma = sigma_known
)
# Treatment prior remains non-informative
prior_treatment <- prior_noninf
summary(prior_placebo)
#> mean sd 2.5% 50.0% 97.5%
#> -0.01083871 0.46144620 -1.15034971 -0.01312827 1.15034975Populations
population_generators <- list(
placebo = function(n) {
data.frame(
id = 1:n,
y = rnorm(n, mean = mu_placebo_true, sd = sigma_known),
readout_time = 1
)
},
treatment = function(n) {
data.frame(
id = 1:n,
y = rnorm(n, mean = mu_placebo_true + delta_true, sd = sigma_known),
readout_time = 1
)
}
)Trial Parameters
enrollment_fn and dropout_fn supply the
inter-arrival and time-to-dropout distributions for individual subjects.
These are passed to replicate_trial(), which uses them when
building each replicate’s enrollment schedule.
Final analysis trigger (Bayesian Go/No-Go)
Only one trigger is added at final analysis.
RBesT::pmixdiff(post_treat, post_placebo, delta, lower.tail = FALSE)
evaluates P(μ_T − μ_P > δ | data) by numerical integration over the
mixture posterior distributions for treatment and placebo.
qmixdiff() computes quantiles of the same posterior
difference distribution, yielding a 95% credible interval for Δ = μ_T −
μ_P.
analysis_generators <- list(
final = list(
trigger = rlang::exprs(
sum(!is.na(enroll_time)) >= !!sample_size
),
analysis = function(df, timer) {
dat <- df |>
dplyr::filter(!is.na(enroll_time)) |>
dplyr::mutate(arm = factor(arm, levels = c("placebo", "treatment")))
y_p <- dat$y[dat$arm == "placebo"]
y_t <- dat$y[dat$arm == "treatment"]
n_p <- length(y_p)
n_t <- length(y_t)
m_p <- mean(y_p)
m_t <- mean(y_t)
# Posterior for each arm mean
post_placebo <- RBesT::postmix(prior_placebo, n = n_p, m = m_p)
post_treat <- RBesT::postmix(prior_treatment, n = n_t, m = m_t)
# Posterior probability for treatment effect exceeding delta
prob_delta <- RBesT::pmixdiff(post_treat, post_placebo, delta, lower.tail = FALSE)
# Posterior summaries for Delta = mu_T - mu_P
delta_ci <- RBesT::qmixdiff(post_treat, post_placebo, c(0.025, 0.5, 0.975))
data.frame(
scenario,
n_placebo = n_p,
n_treatment = n_t,
mean_placebo = m_p,
mean_treatment = m_t,
post_prob_delta = prob_delta,
delta_q025 = delta_ci[1],
delta_q500 = delta_ci[2],
delta_q975 = delta_ci[3],
decision_go = as.integer(prob_delta >= gamma),
stringsAsFactors = FALSE
)
}
)
)Trial
trials <- replicate_trial(
trial_name = "example_7_bayes_two_arm_fixed",
sample_size = sample_size,
arms = arms,
allocation = allocation,
enrollment = enrollment_fn,
dropout = dropout_fn,
analysis_generators = analysis_generators,
population_generators = population_generators,
n = 3
)Simulate
run_trials(trials)
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> Using default prior reference scale 1
#> [[1]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: example_7_bayes_two_arm_fixed_1
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[2]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: example_7_bayes_two_arm_fixed_2
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[3]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: example_7_bayes_two_arm_fixed_3
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6Results
collect_results() row-binds analysis outputs across all
replicates and prepends replicate (integer index),
timepoint (calendar time at which the analysis fired), and
analysis (the analysis name) to each row.
post_prob_delta is the key decision metric: values at or
above gamma = 0.8 yield decision_go = 1 (Go)
and values below yield decision_go = 0 (No-Go). The
credible interval columns delta_q025,
delta_q500, and delta_q975 characterise the
posterior uncertainty about the treatment effect Δ; with n=80 and σ=1,
the posterior is still relatively wide. In practice you would run
thousands of replicates and report the Go probability under both the
null (Δ = 0) and the alternative (e.g., Δ = 0.30) to assess the design’s
operating characteristics. See Example 8
for a seamless design that builds on this Bayesian decision rule.
replicate_results <- collect_results(trials)
replicate_results
#> replicate timepoint analysis sample_size allocation delta_true delta gamma
#> 1 1 78.01039 final 80 1, 1 0.3 0.1 0.8
#> 2 1 166.62671 final 80 1, 1 0.3 0.1 0.8
#> 3 1 274.74269 final 80 1, 1 0.3 0.1 0.8
#> 4 1 341.00793 final 80 1, 1 0.3 0.1 0.8
#> 5 1 397.64554 final 80 1, 1 0.3 0.1 0.8
#> 6 1 542.07960 final 80 1, 1 0.3 0.1 0.8
#> 7 1 993.52447 final 80 1, 1 0.3 0.1 0.8
#> 8 1 1030.14181 final 80 1, 1 0.3 0.1 0.8
#> 9 1 1046.09099 final 80 1, 1 0.3 0.1 0.8
#> 10 1 1266.02529 final 80 1, 1 0.3 0.1 0.8
#> 11 1 1600.54126 final 80 1, 1 0.3 0.1 0.8
#> 12 1 1700.98321 final 80 1, 1 0.3 0.1 0.8
#> 13 1 1765.90494 final 80 1, 1 0.3 0.1 0.8
#> 14 1 1777.88160 final 80 1, 1 0.3 0.1 0.8
#> 15 1 1853.14483 final 80 1, 1 0.3 0.1 0.8
#> 16 1 1890.49139 final 80 1, 1 0.3 0.1 0.8
#> 17 1 1988.66721 final 80 1, 1 0.3 0.1 0.8
#> 18 1 2048.00362 final 80 1, 1 0.3 0.1 0.8
#> 19 1 2204.84397 final 80 1, 1 0.3 0.1 0.8
#> 20 1 2205.45194 final 80 1, 1 0.3 0.1 0.8
#> 21 1 2274.05430 final 80 1, 1 0.3 0.1 0.8
#> 22 1 2304.29670 final 80 1, 1 0.3 0.1 0.8
#> 23 1 2494.47980 final 80 1, 1 0.3 0.1 0.8
#> 24 1 2589.90916 final 80 1, 1 0.3 0.1 0.8
#> 25 1 2609.87077 final 80 1, 1 0.3 0.1 0.8
#> 26 1 2802.56772 final 80 1, 1 0.3 0.1 0.8
#> 27 1 2814.75858 final 80 1, 1 0.3 0.1 0.8
#> 28 1 2875.69398 final 80 1, 1 0.3 0.1 0.8
#> 29 1 3386.66459 final 80 1, 1 0.3 0.1 0.8
#> 30 1 3619.37171 final 80 1, 1 0.3 0.1 0.8
#> 31 1 3691.53725 final 80 1, 1 0.3 0.1 0.8
#> 32 1 4082.85376 final 80 1, 1 0.3 0.1 0.8
#> 33 1 4268.87048 final 80 1, 1 0.3 0.1 0.8
#> 34 1 4320.64179 final 80 1, 1 0.3 0.1 0.8
#> 35 1 4526.06241 final 80 1, 1 0.3 0.1 0.8
#> 36 1 4567.62111 final 80 1, 1 0.3 0.1 0.8
#> 37 1 4668.73265 final 80 1, 1 0.3 0.1 0.8
#> 38 1 4755.41828 final 80 1, 1 0.3 0.1 0.8
#> 39 1 4811.44714 final 80 1, 1 0.3 0.1 0.8
#> 40 1 4848.88559 final 80 1, 1 0.3 0.1 0.8
#> 41 1 4948.88540 final 80 1, 1 0.3 0.1 0.8
#> 42 1 5227.15583 final 80 1, 1 0.3 0.1 0.8
#> 43 1 5257.56630 final 80 1, 1 0.3 0.1 0.8
#> 44 1 5355.23288 final 80 1, 1 0.3 0.1 0.8
#> 45 1 5416.18547 final 80 1, 1 0.3 0.1 0.8
#> 46 1 5584.38678 final 80 1, 1 0.3 0.1 0.8
#> 47 1 5620.74779 final 80 1, 1 0.3 0.1 0.8
#> 48 1 5835.61550 final 80 1, 1 0.3 0.1 0.8
#> 49 1 5888.72060 final 80 1, 1 0.3 0.1 0.8
#> 50 1 5898.19732 final 80 1, 1 0.3 0.1 0.8
#> 51 1 5915.49912 final 80 1, 1 0.3 0.1 0.8
#> 52 1 5966.22425 final 80 1, 1 0.3 0.1 0.8
#> 53 1 6001.27279 final 80 1, 1 0.3 0.1 0.8
#> 54 1 6216.09594 final 80 1, 1 0.3 0.1 0.8
#> 55 1 6461.56539 final 80 1, 1 0.3 0.1 0.8
#> 56 1 6604.41916 final 80 1, 1 0.3 0.1 0.8
#> 57 1 6625.90225 final 80 1, 1 0.3 0.1 0.8
#> 58 1 6626.04747 final 80 1, 1 0.3 0.1 0.8
#> 59 1 6912.07968 final 80 1, 1 0.3 0.1 0.8
#> 60 1 7008.96686 final 80 1, 1 0.3 0.1 0.8
#> 61 1 7076.90002 final 80 1, 1 0.3 0.1 0.8
#> 62 1 7187.95434 final 80 1, 1 0.3 0.1 0.8
#> 63 1 7275.23947 final 80 1, 1 0.3 0.1 0.8
#> 64 1 7276.16337 final 80 1, 1 0.3 0.1 0.8
#> 65 1 7312.20614 final 80 1, 1 0.3 0.1 0.8
#> 66 1 7338.43520 final 80 1, 1 0.3 0.1 0.8
#> 67 1 7339.54863 final 80 1, 1 0.3 0.1 0.8
#> 68 1 7504.97403 final 80 1, 1 0.3 0.1 0.8
#> 69 1 7569.27389 final 80 1, 1 0.3 0.1 0.8
#> 70 1 7708.18198 final 80 1, 1 0.3 0.1 0.8
#> 71 1 7776.55727 final 80 1, 1 0.3 0.1 0.8
#> 72 1 7862.20170 final 80 1, 1 0.3 0.1 0.8
#> 73 1 7909.30003 final 80 1, 1 0.3 0.1 0.8
#> 74 1 7959.38094 final 80 1, 1 0.3 0.1 0.8
#> 75 1 8061.10855 final 80 1, 1 0.3 0.1 0.8
#> 76 1 8100.11077 final 80 1, 1 0.3 0.1 0.8
#> 77 1 8198.15124 final 80 1, 1 0.3 0.1 0.8
#> 78 1 8257.56559 final 80 1, 1 0.3 0.1 0.8
#> 79 1 8379.88677 final 80 1, 1 0.3 0.1 0.8
#> 80 1 8420.19824 final 80 1, 1 0.3 0.1 0.8
#> 81 2 72.27821 final 80 1, 1 0.3 0.1 0.8
#> 82 2 232.93573 final 80 1, 1 0.3 0.1 0.8
#> 83 2 268.97090 final 80 1, 1 0.3 0.1 0.8
#> 84 2 303.69245 final 80 1, 1 0.3 0.1 0.8
#> 85 2 370.99856 final 80 1, 1 0.3 0.1 0.8
#> 86 2 425.48186 final 80 1, 1 0.3 0.1 0.8
#> 87 2 469.94651 final 80 1, 1 0.3 0.1 0.8
#> 88 2 523.53947 final 80 1, 1 0.3 0.1 0.8
#> 89 2 551.41397 final 80 1, 1 0.3 0.1 0.8
#> 90 2 760.20443 final 80 1, 1 0.3 0.1 0.8
#> 91 2 765.20555 final 80 1, 1 0.3 0.1 0.8
#> 92 2 876.66320 final 80 1, 1 0.3 0.1 0.8
#> 93 2 923.23818 final 80 1, 1 0.3 0.1 0.8
#> 94 2 957.10604 final 80 1, 1 0.3 0.1 0.8
#> 95 2 1110.50539 final 80 1, 1 0.3 0.1 0.8
#> 96 2 1412.34485 final 80 1, 1 0.3 0.1 0.8
#> 97 2 1611.75866 final 80 1, 1 0.3 0.1 0.8
#> 98 2 1642.23767 final 80 1, 1 0.3 0.1 0.8
#> 99 2 1756.04808 final 80 1, 1 0.3 0.1 0.8
#> 100 2 1779.66003 final 80 1, 1 0.3 0.1 0.8
#> 101 2 1820.23743 final 80 1, 1 0.3 0.1 0.8
#> 102 2 1841.89988 final 80 1, 1 0.3 0.1 0.8
#> 103 2 1886.16137 final 80 1, 1 0.3 0.1 0.8
#> 104 2 1887.57464 final 80 1, 1 0.3 0.1 0.8
#> 105 2 1961.61067 final 80 1, 1 0.3 0.1 0.8
#> 106 2 2164.22037 final 80 1, 1 0.3 0.1 0.8
#> 107 2 2175.48914 final 80 1, 1 0.3 0.1 0.8
#> 108 2 2457.28059 final 80 1, 1 0.3 0.1 0.8
#> 109 2 2458.24513 final 80 1, 1 0.3 0.1 0.8
#> 110 2 2461.51360 final 80 1, 1 0.3 0.1 0.8
#> 111 2 2485.92905 final 80 1, 1 0.3 0.1 0.8
#> 112 2 2525.24309 final 80 1, 1 0.3 0.1 0.8
#> 113 2 2622.41460 final 80 1, 1 0.3 0.1 0.8
#> 114 2 2830.12043 final 80 1, 1 0.3 0.1 0.8
#> 115 2 2833.16944 final 80 1, 1 0.3 0.1 0.8
#> 116 2 2906.41171 final 80 1, 1 0.3 0.1 0.8
#> 117 2 2926.71198 final 80 1, 1 0.3 0.1 0.8
#> 118 2 3053.92052 final 80 1, 1 0.3 0.1 0.8
#> 119 2 3153.91626 final 80 1, 1 0.3 0.1 0.8
#> 120 2 3174.25744 final 80 1, 1 0.3 0.1 0.8
#> 121 2 3264.25914 final 80 1, 1 0.3 0.1 0.8
#> 122 2 3423.66688 final 80 1, 1 0.3 0.1 0.8
#> 123 2 3685.21561 final 80 1, 1 0.3 0.1 0.8
#> 124 2 3826.62390 final 80 1, 1 0.3 0.1 0.8
#> 125 2 3835.03646 final 80 1, 1 0.3 0.1 0.8
#> 126 2 4064.78948 final 80 1, 1 0.3 0.1 0.8
#> 127 2 4076.08980 final 80 1, 1 0.3 0.1 0.8
#> 128 2 4092.68989 final 80 1, 1 0.3 0.1 0.8
#> 129 2 4132.61804 final 80 1, 1 0.3 0.1 0.8
#> 130 2 4198.34451 final 80 1, 1 0.3 0.1 0.8
#> 131 2 4246.89554 final 80 1, 1 0.3 0.1 0.8
#> 132 2 4295.48743 final 80 1, 1 0.3 0.1 0.8
#> 133 2 4368.81326 final 80 1, 1 0.3 0.1 0.8
#> 134 2 4462.32322 final 80 1, 1 0.3 0.1 0.8
#> 135 2 4652.33416 final 80 1, 1 0.3 0.1 0.8
#> 136 2 4655.58399 final 80 1, 1 0.3 0.1 0.8
#> 137 2 4962.54541 final 80 1, 1 0.3 0.1 0.8
#> 138 2 5019.79414 final 80 1, 1 0.3 0.1 0.8
#> 139 2 5116.77034 final 80 1, 1 0.3 0.1 0.8
#> 140 2 5209.60148 final 80 1, 1 0.3 0.1 0.8
#> 141 2 5275.25817 final 80 1, 1 0.3 0.1 0.8
#> 142 2 5452.44413 final 80 1, 1 0.3 0.1 0.8
#> 143 2 5538.56539 final 80 1, 1 0.3 0.1 0.8
#> 144 2 5636.65654 final 80 1, 1 0.3 0.1 0.8
#> 145 2 5717.70576 final 80 1, 1 0.3 0.1 0.8
#> 146 2 5769.81900 final 80 1, 1 0.3 0.1 0.8
#> 147 2 5775.07185 final 80 1, 1 0.3 0.1 0.8
#> 148 2 5825.52155 final 80 1, 1 0.3 0.1 0.8
#> 149 2 5832.12600 final 80 1, 1 0.3 0.1 0.8
#> 150 2 5835.52635 final 80 1, 1 0.3 0.1 0.8
#> 151 2 5865.30222 final 80 1, 1 0.3 0.1 0.8
#> 152 2 5895.14827 final 80 1, 1 0.3 0.1 0.8
#> 153 2 6320.35624 final 80 1, 1 0.3 0.1 0.8
#> 154 2 6334.37112 final 80 1, 1 0.3 0.1 0.8
#> 155 2 6478.15678 final 80 1, 1 0.3 0.1 0.8
#> 156 2 6509.34144 final 80 1, 1 0.3 0.1 0.8
#> 157 2 6511.41412 final 80 1, 1 0.3 0.1 0.8
#> 158 2 6553.27902 final 80 1, 1 0.3 0.1 0.8
#> 159 2 6584.02090 final 80 1, 1 0.3 0.1 0.8
#> 160 2 6833.71939 final 80 1, 1 0.3 0.1 0.8
#> 161 2 7185.92084 final 80 1, 1 0.3 0.1 0.8
#> 162 3 89.63501 final 80 1, 1 0.3 0.1 0.8
#> 163 3 92.44571 final 80 1, 1 0.3 0.1 0.8
#> 164 3 107.27336 final 80 1, 1 0.3 0.1 0.8
#> 165 3 155.03158 final 80 1, 1 0.3 0.1 0.8
#> 166 3 163.22004 final 80 1, 1 0.3 0.1 0.8
#> 167 3 211.50328 final 80 1, 1 0.3 0.1 0.8
#> 168 3 274.37196 final 80 1, 1 0.3 0.1 0.8
#> 169 3 350.40933 final 80 1, 1 0.3 0.1 0.8
#> 170 3 391.71272 final 80 1, 1 0.3 0.1 0.8
#> 171 3 439.60209 final 80 1, 1 0.3 0.1 0.8
#> 172 3 485.74316 final 80 1, 1 0.3 0.1 0.8
#> 173 3 526.20723 final 80 1, 1 0.3 0.1 0.8
#> 174 3 602.91463 final 80 1, 1 0.3 0.1 0.8
#> 175 3 653.13648 final 80 1, 1 0.3 0.1 0.8
#> 176 3 768.94793 final 80 1, 1 0.3 0.1 0.8
#> 177 3 786.14661 final 80 1, 1 0.3 0.1 0.8
#> 178 3 866.24212 final 80 1, 1 0.3 0.1 0.8
#> 179 3 872.28363 final 80 1, 1 0.3 0.1 0.8
#> 180 3 934.57354 final 80 1, 1 0.3 0.1 0.8
#> 181 3 987.19940 final 80 1, 1 0.3 0.1 0.8
#> 182 3 1017.71064 final 80 1, 1 0.3 0.1 0.8
#> 183 3 1200.11344 final 80 1, 1 0.3 0.1 0.8
#> 184 3 1306.67993 final 80 1, 1 0.3 0.1 0.8
#> 185 3 1360.44531 final 80 1, 1 0.3 0.1 0.8
#> 186 3 1362.00797 final 80 1, 1 0.3 0.1 0.8
#> 187 3 1388.21354 final 80 1, 1 0.3 0.1 0.8
#> 188 3 1578.95235 final 80 1, 1 0.3 0.1 0.8
#> 189 3 1599.65901 final 80 1, 1 0.3 0.1 0.8
#> 190 3 1605.60287 final 80 1, 1 0.3 0.1 0.8
#> 191 3 1609.82020 final 80 1, 1 0.3 0.1 0.8
#> 192 3 1648.13453 final 80 1, 1 0.3 0.1 0.8
#> 193 3 1748.21076 final 80 1, 1 0.3 0.1 0.8
#> 194 3 1844.85217 final 80 1, 1 0.3 0.1 0.8
#> 195 3 1899.01464 final 80 1, 1 0.3 0.1 0.8
#> 196 3 1918.59842 final 80 1, 1 0.3 0.1 0.8
#> 197 3 2025.58275 final 80 1, 1 0.3 0.1 0.8
#> 198 3 2088.78760 final 80 1, 1 0.3 0.1 0.8
#> 199 3 2161.93444 final 80 1, 1 0.3 0.1 0.8
#> 200 3 2299.87567 final 80 1, 1 0.3 0.1 0.8
#> 201 3 2508.37171 final 80 1, 1 0.3 0.1 0.8
#> 202 3 2552.18321 final 80 1, 1 0.3 0.1 0.8
#> 203 3 2623.58744 final 80 1, 1 0.3 0.1 0.8
#> 204 3 2807.08992 final 80 1, 1 0.3 0.1 0.8
#> 205 3 2895.90606 final 80 1, 1 0.3 0.1 0.8
#> 206 3 3291.70563 final 80 1, 1 0.3 0.1 0.8
#> 207 3 3312.89467 final 80 1, 1 0.3 0.1 0.8
#> 208 3 3405.24745 final 80 1, 1 0.3 0.1 0.8
#> 209 3 3422.07365 final 80 1, 1 0.3 0.1 0.8
#> 210 3 3532.45172 final 80 1, 1 0.3 0.1 0.8
#> 211 3 3560.52150 final 80 1, 1 0.3 0.1 0.8
#> 212 3 3620.08800 final 80 1, 1 0.3 0.1 0.8
#> 213 3 3938.12837 final 80 1, 1 0.3 0.1 0.8
#> 214 3 3963.60226 final 80 1, 1 0.3 0.1 0.8
#> 215 3 3987.83905 final 80 1, 1 0.3 0.1 0.8
#> 216 3 4221.72960 final 80 1, 1 0.3 0.1 0.8
#> 217 3 4349.81402 final 80 1, 1 0.3 0.1 0.8
#> 218 3 4719.78920 final 80 1, 1 0.3 0.1 0.8
#> 219 3 4750.43111 final 80 1, 1 0.3 0.1 0.8
#> 220 3 4880.62711 final 80 1, 1 0.3 0.1 0.8
#> 221 3 4934.73439 final 80 1, 1 0.3 0.1 0.8
#> 222 3 4960.69961 final 80 1, 1 0.3 0.1 0.8
#> 223 3 4965.92877 final 80 1, 1 0.3 0.1 0.8
#> 224 3 4974.81858 final 80 1, 1 0.3 0.1 0.8
#> 225 3 5021.99314 final 80 1, 1 0.3 0.1 0.8
#> 226 3 5072.50231 final 80 1, 1 0.3 0.1 0.8
#> 227 3 5072.95339 final 80 1, 1 0.3 0.1 0.8
#> 228 3 5121.03919 final 80 1, 1 0.3 0.1 0.8
#> 229 3 5190.72293 final 80 1, 1 0.3 0.1 0.8
#> 230 3 5257.31143 final 80 1, 1 0.3 0.1 0.8
#> 231 3 5338.31098 final 80 1, 1 0.3 0.1 0.8
#> 232 3 5359.66163 final 80 1, 1 0.3 0.1 0.8
#> 233 3 5413.62218 final 80 1, 1 0.3 0.1 0.8
#> 234 3 5528.46797 final 80 1, 1 0.3 0.1 0.8
#> 235 3 5584.10656 final 80 1, 1 0.3 0.1 0.8
#> 236 3 5608.75695 final 80 1, 1 0.3 0.1 0.8
#> 237 3 5717.07576 final 80 1, 1 0.3 0.1 0.8
#> 238 3 6044.72272 final 80 1, 1 0.3 0.1 0.8
#> 239 3 6105.39837 final 80 1, 1 0.3 0.1 0.8
#> 240 3 6201.62981 final 80 1, 1 0.3 0.1 0.8
#> 241 3 6625.67574 final 80 1, 1 0.3 0.1 0.8
#> 242 3 6701.99306 final 80 1, 1 0.3 0.1 0.8
#> n_placebo n_treatment mean_placebo mean_treatment post_prob_delta
#> 1 40 40 0.1581546 0.3864720 0.8826182
#> 2 40 40 0.1581546 0.3864720 0.8826182
#> 3 40 40 0.1581546 0.3864720 0.8826182
#> 4 40 40 0.1581546 0.3864720 0.8826182
#> 5 40 40 0.1581546 0.3864720 0.8826182
#> 6 40 40 0.1581546 0.3864720 0.8826182
#> 7 40 40 0.1581546 0.3864720 0.8826182
#> 8 40 40 0.1581546 0.3864720 0.8826182
#> 9 40 40 0.1581546 0.3864720 0.8826182
#> 10 40 40 0.1581546 0.3864720 0.8826182
#> 11 40 40 0.1581546 0.3864720 0.8826182
#> 12 40 40 0.1581546 0.3864720 0.8826182
#> 13 40 40 0.1581546 0.3864720 0.8826182
#> 14 40 40 0.1581546 0.3864720 0.8826182
#> 15 40 40 0.1581546 0.3864720 0.8826182
#> 16 40 40 0.1581546 0.3864720 0.8826182
#> 17 40 40 0.1581546 0.3864720 0.8826182
#> 18 40 40 0.1581546 0.3864720 0.8826182
#> 19 40 40 0.1581546 0.3864720 0.8826182
#> 20 40 40 0.1581546 0.3864720 0.8826182
#> 21 40 40 0.1581546 0.3864720 0.8826182
#> 22 40 40 0.1581546 0.3864720 0.8826182
#> 23 40 40 0.1581546 0.3864720 0.8826182
#> 24 40 40 0.1581546 0.3864720 0.8826182
#> 25 40 40 0.1581546 0.3864720 0.8826182
#> 26 40 40 0.1581546 0.3864720 0.8826182
#> 27 40 40 0.1581546 0.3864720 0.8826182
#> 28 40 40 0.1581546 0.3864720 0.8826182
#> 29 40 40 0.1581546 0.3864720 0.8826182
#> 30 40 40 0.1581546 0.3864720 0.8826182
#> 31 40 40 0.1581546 0.3864720 0.8826182
#> 32 40 40 0.1581546 0.3864720 0.8826182
#> 33 40 40 0.1581546 0.3864720 0.8826182
#> 34 40 40 0.1581546 0.3864720 0.8826182
#> 35 40 40 0.1581546 0.3864720 0.8826182
#> 36 40 40 0.1581546 0.3864720 0.8826182
#> 37 40 40 0.1581546 0.3864720 0.8826182
#> 38 40 40 0.1581546 0.3864720 0.8826182
#> 39 40 40 0.1581546 0.3864720 0.8826182
#> 40 40 40 0.1581546 0.3864720 0.8826182
#> 41 40 40 0.1581546 0.3864720 0.8826182
#> 42 40 40 0.1581546 0.3864720 0.8826182
#> 43 40 40 0.1581546 0.3864720 0.8826182
#> 44 40 40 0.1581546 0.3864720 0.8826182
#> 45 40 40 0.1581546 0.3864720 0.8826182
#> 46 40 40 0.1581546 0.3864720 0.8826182
#> 47 40 40 0.1581546 0.3864720 0.8826182
#> 48 40 40 0.1581546 0.3864720 0.8826182
#> 49 40 40 0.1581546 0.3864720 0.8826182
#> 50 40 40 0.1581546 0.3864720 0.8826182
#> 51 40 40 0.1581546 0.3864720 0.8826182
#> 52 40 40 0.1581546 0.3864720 0.8826182
#> 53 40 40 0.1581546 0.3864720 0.8826182
#> 54 40 40 0.1581546 0.3864720 0.8826182
#> 55 40 40 0.1581546 0.3864720 0.8826182
#> 56 40 40 0.1581546 0.3864720 0.8826182
#> 57 40 40 0.1581546 0.3864720 0.8826182
#> 58 40 40 0.1581546 0.3864720 0.8826182
#> 59 40 40 0.1581546 0.3864720 0.8826182
#> 60 40 40 0.1581546 0.3864720 0.8826182
#> 61 40 40 0.1581546 0.3864720 0.8826182
#> 62 40 40 0.1581546 0.3864720 0.8826182
#> 63 40 40 0.1581546 0.3864720 0.8826182
#> 64 40 40 0.1581546 0.3864720 0.8826182
#> 65 40 40 0.1581546 0.3864720 0.8826182
#> 66 40 40 0.1581546 0.3864720 0.8826182
#> 67 40 40 0.1581546 0.3864720 0.8826182
#> 68 40 40 0.1581546 0.3864720 0.8826182
#> 69 40 40 0.1581546 0.3864720 0.8826182
#> 70 40 40 0.1581546 0.3864720 0.8826182
#> 71 40 40 0.1581546 0.3864720 0.8826182
#> 72 40 40 0.1581546 0.3864720 0.8826182
#> 73 40 40 0.1581546 0.3864720 0.8826182
#> 74 40 40 0.1581546 0.3864720 0.8826182
#> 75 40 40 0.1581546 0.3864720 0.8826182
#> 76 40 40 0.1581546 0.3864720 0.8826182
#> 77 40 40 0.1581546 0.3864720 0.8826182
#> 78 40 40 0.1581546 0.3864720 0.8826182
#> 79 40 40 0.1581546 0.3864720 0.8826182
#> 80 40 40 0.1581546 0.3864720 0.8826182
#> 81 40 40 0.1669418 0.1654918 0.5048670
#> 82 40 40 0.1669418 0.1654918 0.5048670
#> 83 40 40 0.1669418 0.1654918 0.5048670
#> 84 40 40 0.1669418 0.1654918 0.5048670
#> 85 40 40 0.1669418 0.1654918 0.5048670
#> 86 40 40 0.1669418 0.1654918 0.5048670
#> 87 40 40 0.1669418 0.1654918 0.5048670
#> 88 40 40 0.1669418 0.1654918 0.5048670
#> 89 40 40 0.1669418 0.1654918 0.5048670
#> 90 40 40 0.1669418 0.1654918 0.5048670
#> 91 40 40 0.1669418 0.1654918 0.5048670
#> 92 40 40 0.1669418 0.1654918 0.5048670
#> 93 40 40 0.1669418 0.1654918 0.5048670
#> 94 40 40 0.1669418 0.1654918 0.5048670
#> 95 40 40 0.1669418 0.1654918 0.5048670
#> 96 40 40 0.1669418 0.1654918 0.5048670
#> 97 40 40 0.1669418 0.1654918 0.5048670
#> 98 40 40 0.1669418 0.1654918 0.5048670
#> 99 40 40 0.1669418 0.1654918 0.5048670
#> 100 40 40 0.1669418 0.1654918 0.5048670
#> 101 40 40 0.1669418 0.1654918 0.5048670
#> 102 40 40 0.1669418 0.1654918 0.5048670
#> 103 40 40 0.1669418 0.1654918 0.5048670
#> 104 40 40 0.1669418 0.1654918 0.5048670
#> 105 40 40 0.1669418 0.1654918 0.5048670
#> 106 40 40 0.1669418 0.1654918 0.5048670
#> 107 40 40 0.1669418 0.1654918 0.5048670
#> 108 40 40 0.1669418 0.1654918 0.5048670
#> 109 40 40 0.1669418 0.1654918 0.5048670
#> 110 40 40 0.1669418 0.1654918 0.5048670
#> 111 40 40 0.1669418 0.1654918 0.5048670
#> 112 40 40 0.1669418 0.1654918 0.5048670
#> 113 40 40 0.1669418 0.1654918 0.5048670
#> 114 40 40 0.1669418 0.1654918 0.5048670
#> 115 40 40 0.1669418 0.1654918 0.5048670
#> 116 40 40 0.1669418 0.1654918 0.5048670
#> 117 40 40 0.1669418 0.1654918 0.5048670
#> 118 40 40 0.1669418 0.1654918 0.5048670
#> 119 40 40 0.1669418 0.1654918 0.5048670
#> 120 40 40 0.1669418 0.1654918 0.5048670
#> 121 40 40 0.1669418 0.1654918 0.5048670
#> 122 40 40 0.1669418 0.1654918 0.5048670
#> 123 40 40 0.1669418 0.1654918 0.5048670
#> 124 40 40 0.1669418 0.1654918 0.5048670
#> 125 40 40 0.1669418 0.1654918 0.5048670
#> 126 40 40 0.1669418 0.1654918 0.5048670
#> 127 40 40 0.1669418 0.1654918 0.5048670
#> 128 40 40 0.1669418 0.1654918 0.5048670
#> 129 40 40 0.1669418 0.1654918 0.5048670
#> 130 40 40 0.1669418 0.1654918 0.5048670
#> 131 40 40 0.1669418 0.1654918 0.5048670
#> 132 40 40 0.1669418 0.1654918 0.5048670
#> 133 40 40 0.1669418 0.1654918 0.5048670
#> 134 40 40 0.1669418 0.1654918 0.5048670
#> 135 40 40 0.1669418 0.1654918 0.5048670
#> 136 40 40 0.1669418 0.1654918 0.5048670
#> 137 40 40 0.1669418 0.1654918 0.5048670
#> 138 40 40 0.1669418 0.1654918 0.5048670
#> 139 40 40 0.1669418 0.1654918 0.5048670
#> 140 40 40 0.1669418 0.1654918 0.5048670
#> 141 40 40 0.1669418 0.1654918 0.5048670
#> 142 40 40 0.1669418 0.1654918 0.5048670
#> 143 40 40 0.1669418 0.1654918 0.5048670
#> 144 40 40 0.1669418 0.1654918 0.5048670
#> 145 40 40 0.1669418 0.1654918 0.5048670
#> 146 40 40 0.1669418 0.1654918 0.5048670
#> 147 40 40 0.1669418 0.1654918 0.5048670
#> 148 40 40 0.1669418 0.1654918 0.5048670
#> 149 40 40 0.1669418 0.1654918 0.5048670
#> 150 40 40 0.1669418 0.1654918 0.5048670
#> 151 40 40 0.1669418 0.1654918 0.5048670
#> 152 40 40 0.1669418 0.1654918 0.5048670
#> 153 40 40 0.1669418 0.1654918 0.5048670
#> 154 40 40 0.1669418 0.1654918 0.5048670
#> 155 40 40 0.1669418 0.1654918 0.5048670
#> 156 40 40 0.1669418 0.1654918 0.5048670
#> 157 40 40 0.1669418 0.1654918 0.5048670
#> 158 40 40 0.1669418 0.1654918 0.5048670
#> 159 40 40 0.1669418 0.1654918 0.5048670
#> 160 40 40 0.1669418 0.1654918 0.5048670
#> 161 40 40 0.1669418 0.1654918 0.5048670
#> 162 40 40 -0.4034483 0.3106430 0.9825414
#> 163 40 40 -0.4034483 0.3106430 0.9825414
#> 164 40 40 -0.4034483 0.3106430 0.9825414
#> 165 40 40 -0.4034483 0.3106430 0.9825414
#> 166 40 40 -0.4034483 0.3106430 0.9825414
#> 167 40 40 -0.4034483 0.3106430 0.9825414
#> 168 40 40 -0.4034483 0.3106430 0.9825414
#> 169 40 40 -0.4034483 0.3106430 0.9825414
#> 170 40 40 -0.4034483 0.3106430 0.9825414
#> 171 40 40 -0.4034483 0.3106430 0.9825414
#> 172 40 40 -0.4034483 0.3106430 0.9825414
#> 173 40 40 -0.4034483 0.3106430 0.9825414
#> 174 40 40 -0.4034483 0.3106430 0.9825414
#> 175 40 40 -0.4034483 0.3106430 0.9825414
#> 176 40 40 -0.4034483 0.3106430 0.9825414
#> 177 40 40 -0.4034483 0.3106430 0.9825414
#> 178 40 40 -0.4034483 0.3106430 0.9825414
#> 179 40 40 -0.4034483 0.3106430 0.9825414
#> 180 40 40 -0.4034483 0.3106430 0.9825414
#> 181 40 40 -0.4034483 0.3106430 0.9825414
#> 182 40 40 -0.4034483 0.3106430 0.9825414
#> 183 40 40 -0.4034483 0.3106430 0.9825414
#> 184 40 40 -0.4034483 0.3106430 0.9825414
#> 185 40 40 -0.4034483 0.3106430 0.9825414
#> 186 40 40 -0.4034483 0.3106430 0.9825414
#> 187 40 40 -0.4034483 0.3106430 0.9825414
#> 188 40 40 -0.4034483 0.3106430 0.9825414
#> 189 40 40 -0.4034483 0.3106430 0.9825414
#> 190 40 40 -0.4034483 0.3106430 0.9825414
#> 191 40 40 -0.4034483 0.3106430 0.9825414
#> 192 40 40 -0.4034483 0.3106430 0.9825414
#> 193 40 40 -0.4034483 0.3106430 0.9825414
#> 194 40 40 -0.4034483 0.3106430 0.9825414
#> 195 40 40 -0.4034483 0.3106430 0.9825414
#> 196 40 40 -0.4034483 0.3106430 0.9825414
#> 197 40 40 -0.4034483 0.3106430 0.9825414
#> 198 40 40 -0.4034483 0.3106430 0.9825414
#> 199 40 40 -0.4034483 0.3106430 0.9825414
#> 200 40 40 -0.4034483 0.3106430 0.9825414
#> 201 40 40 -0.4034483 0.3106430 0.9825414
#> 202 40 40 -0.4034483 0.3106430 0.9825414
#> 203 40 40 -0.4034483 0.3106430 0.9825414
#> 204 40 40 -0.4034483 0.3106430 0.9825414
#> 205 40 40 -0.4034483 0.3106430 0.9825414
#> 206 40 40 -0.4034483 0.3106430 0.9825414
#> 207 40 40 -0.4034483 0.3106430 0.9825414
#> 208 40 40 -0.4034483 0.3106430 0.9825414
#> 209 40 40 -0.4034483 0.3106430 0.9825414
#> 210 40 40 -0.4034483 0.3106430 0.9825414
#> 211 40 40 -0.4034483 0.3106430 0.9825414
#> 212 40 40 -0.4034483 0.3106430 0.9825414
#> 213 40 40 -0.4034483 0.3106430 0.9825414
#> 214 40 40 -0.4034483 0.3106430 0.9825414
#> 215 40 40 -0.4034483 0.3106430 0.9825414
#> 216 40 40 -0.4034483 0.3106430 0.9825414
#> 217 40 40 -0.4034483 0.3106430 0.9825414
#> 218 40 40 -0.4034483 0.3106430 0.9825414
#> 219 40 40 -0.4034483 0.3106430 0.9825414
#> 220 40 40 -0.4034483 0.3106430 0.9825414
#> 221 40 40 -0.4034483 0.3106430 0.9825414
#> 222 40 40 -0.4034483 0.3106430 0.9825414
#> 223 40 40 -0.4034483 0.3106430 0.9825414
#> 224 40 40 -0.4034483 0.3106430 0.9825414
#> 225 40 40 -0.4034483 0.3106430 0.9825414
#> 226 40 40 -0.4034483 0.3106430 0.9825414
#> 227 40 40 -0.4034483 0.3106430 0.9825414
#> 228 40 40 -0.4034483 0.3106430 0.9825414
#> 229 40 40 -0.4034483 0.3106430 0.9825414
#> 230 40 40 -0.4034483 0.3106430 0.9825414
#> 231 40 40 -0.4034483 0.3106430 0.9825414
#> 232 40 40 -0.4034483 0.3106430 0.9825414
#> 233 40 40 -0.4034483 0.3106430 0.9825414
#> 234 40 40 -0.4034483 0.3106430 0.9825414
#> 235 40 40 -0.4034483 0.3106430 0.9825414
#> 236 40 40 -0.4034483 0.3106430 0.9825414
#> 237 40 40 -0.4034483 0.3106430 0.9825414
#> 238 40 40 -0.4034483 0.3106430 0.9825414
#> 239 40 40 -0.4034483 0.3106430 0.9825414
#> 240 40 40 -0.4034483 0.3106430 0.9825414
#> 241 40 40 -0.4034483 0.3106430 0.9825414
#> 242 40 40 -0.4034483 0.3106430 0.9825414
#> delta_q025 delta_q500 delta_q975 decision_go
#> 1 -0.05183437 0.3272105 0.6968002 1
#> 2 -0.05183437 0.3272105 0.6968002 1
#> 3 -0.05183437 0.3272105 0.6968002 1
#> 4 -0.05183437 0.3272105 0.6968002 1
#> 5 -0.05183437 0.3272105 0.6968002 1
#> 6 -0.05183437 0.3272105 0.6968002 1
#> 7 -0.05183437 0.3272105 0.6968002 1
#> 8 -0.05183437 0.3272105 0.6968002 1
#> 9 -0.05183437 0.3272105 0.6968002 1
#> 10 -0.05183437 0.3272105 0.6968002 1
#> 11 -0.05183437 0.3272105 0.6968002 1
#> 12 -0.05183437 0.3272105 0.6968002 1
#> 13 -0.05183437 0.3272105 0.6968002 1
#> 14 -0.05183437 0.3272105 0.6968002 1
#> 15 -0.05183437 0.3272105 0.6968002 1
#> 16 -0.05183437 0.3272105 0.6968002 1
#> 17 -0.05183437 0.3272105 0.6968002 1
#> 18 -0.05183437 0.3272105 0.6968002 1
#> 19 -0.05183437 0.3272105 0.6968002 1
#> 20 -0.05183437 0.3272105 0.6968002 1
#> 21 -0.05183437 0.3272105 0.6968002 1
#> 22 -0.05183437 0.3272105 0.6968002 1
#> 23 -0.05183437 0.3272105 0.6968002 1
#> 24 -0.05183437 0.3272105 0.6968002 1
#> 25 -0.05183437 0.3272105 0.6968002 1
#> 26 -0.05183437 0.3272105 0.6968002 1
#> 27 -0.05183437 0.3272105 0.6968002 1
#> 28 -0.05183437 0.3272105 0.6968002 1
#> 29 -0.05183437 0.3272105 0.6968002 1
#> 30 -0.05183437 0.3272105 0.6968002 1
#> 31 -0.05183437 0.3272105 0.6968002 1
#> 32 -0.05183437 0.3272105 0.6968002 1
#> 33 -0.05183437 0.3272105 0.6968002 1
#> 34 -0.05183437 0.3272105 0.6968002 1
#> 35 -0.05183437 0.3272105 0.6968002 1
#> 36 -0.05183437 0.3272105 0.6968002 1
#> 37 -0.05183437 0.3272105 0.6968002 1
#> 38 -0.05183437 0.3272105 0.6968002 1
#> 39 -0.05183437 0.3272105 0.6968002 1
#> 40 -0.05183437 0.3272105 0.6968002 1
#> 41 -0.05183437 0.3272105 0.6968002 1
#> 42 -0.05183437 0.3272105 0.6968002 1
#> 43 -0.05183437 0.3272105 0.6968002 1
#> 44 -0.05183437 0.3272105 0.6968002 1
#> 45 -0.05183437 0.3272105 0.6968002 1
#> 46 -0.05183437 0.3272105 0.6968002 1
#> 47 -0.05183437 0.3272105 0.6968002 1
#> 48 -0.05183437 0.3272105 0.6968002 1
#> 49 -0.05183437 0.3272105 0.6968002 1
#> 50 -0.05183437 0.3272105 0.6968002 1
#> 51 -0.05183437 0.3272105 0.6968002 1
#> 52 -0.05183437 0.3272105 0.6968002 1
#> 53 -0.05183437 0.3272105 0.6968002 1
#> 54 -0.05183437 0.3272105 0.6968002 1
#> 55 -0.05183437 0.3272105 0.6968002 1
#> 56 -0.05183437 0.3272105 0.6968002 1
#> 57 -0.05183437 0.3272105 0.6968002 1
#> 58 -0.05183437 0.3272105 0.6968002 1
#> 59 -0.05183437 0.3272105 0.6968002 1
#> 60 -0.05183437 0.3272105 0.6968002 1
#> 61 -0.05183437 0.3272105 0.6968002 1
#> 62 -0.05183437 0.3272105 0.6968002 1
#> 63 -0.05183437 0.3272105 0.6968002 1
#> 64 -0.05183437 0.3272105 0.6968002 1
#> 65 -0.05183437 0.3272105 0.6968002 1
#> 66 -0.05183437 0.3272105 0.6968002 1
#> 67 -0.05183437 0.3272105 0.6968002 1
#> 68 -0.05183437 0.3272105 0.6968002 1
#> 69 -0.05183437 0.3272105 0.6968002 1
#> 70 -0.05183437 0.3272105 0.6968002 1
#> 71 -0.05183437 0.3272105 0.6968002 1
#> 72 -0.05183437 0.3272105 0.6968002 1
#> 73 -0.05183437 0.3272105 0.6968002 1
#> 74 -0.05183437 0.3272105 0.6968002 1
#> 75 -0.05183437 0.3272105 0.6968002 1
#> 76 -0.05183437 0.3272105 0.6968002 1
#> 77 -0.05183437 0.3272105 0.6968002 1
#> 78 -0.05183437 0.3272105 0.6968002 1
#> 79 -0.05183437 0.3272105 0.6968002 1
#> 80 -0.05183437 0.3272105 0.6968002 1
#> 81 -0.27784403 0.1023216 0.4720979 0
#> 82 -0.27784403 0.1023216 0.4720979 0
#> 83 -0.27784403 0.1023216 0.4720979 0
#> 84 -0.27784403 0.1023216 0.4720979 0
#> 85 -0.27784403 0.1023216 0.4720979 0
#> 86 -0.27784403 0.1023216 0.4720979 0
#> 87 -0.27784403 0.1023216 0.4720979 0
#> 88 -0.27784403 0.1023216 0.4720979 0
#> 89 -0.27784403 0.1023216 0.4720979 0
#> 90 -0.27784403 0.1023216 0.4720979 0
#> 91 -0.27784403 0.1023216 0.4720979 0
#> 92 -0.27784403 0.1023216 0.4720979 0
#> 93 -0.27784403 0.1023216 0.4720979 0
#> 94 -0.27784403 0.1023216 0.4720979 0
#> 95 -0.27784403 0.1023216 0.4720979 0
#> 96 -0.27784403 0.1023216 0.4720979 0
#> 97 -0.27784403 0.1023216 0.4720979 0
#> 98 -0.27784403 0.1023216 0.4720979 0
#> 99 -0.27784403 0.1023216 0.4720979 0
#> 100 -0.27784403 0.1023216 0.4720979 0
#> 101 -0.27784403 0.1023216 0.4720979 0
#> 102 -0.27784403 0.1023216 0.4720979 0
#> 103 -0.27784403 0.1023216 0.4720979 0
#> 104 -0.27784403 0.1023216 0.4720979 0
#> 105 -0.27784403 0.1023216 0.4720979 0
#> 106 -0.27784403 0.1023216 0.4720979 0
#> 107 -0.27784403 0.1023216 0.4720979 0
#> 108 -0.27784403 0.1023216 0.4720979 0
#> 109 -0.27784403 0.1023216 0.4720979 0
#> 110 -0.27784403 0.1023216 0.4720979 0
#> 111 -0.27784403 0.1023216 0.4720979 0
#> 112 -0.27784403 0.1023216 0.4720979 0
#> 113 -0.27784403 0.1023216 0.4720979 0
#> 114 -0.27784403 0.1023216 0.4720979 0
#> 115 -0.27784403 0.1023216 0.4720979 0
#> 116 -0.27784403 0.1023216 0.4720979 0
#> 117 -0.27784403 0.1023216 0.4720979 0
#> 118 -0.27784403 0.1023216 0.4720979 0
#> 119 -0.27784403 0.1023216 0.4720979 0
#> 120 -0.27784403 0.1023216 0.4720979 0
#> 121 -0.27784403 0.1023216 0.4720979 0
#> 122 -0.27784403 0.1023216 0.4720979 0
#> 123 -0.27784403 0.1023216 0.4720979 0
#> 124 -0.27784403 0.1023216 0.4720979 0
#> 125 -0.27784403 0.1023216 0.4720979 0
#> 126 -0.27784403 0.1023216 0.4720979 0
#> 127 -0.27784403 0.1023216 0.4720979 0
#> 128 -0.27784403 0.1023216 0.4720979 0
#> 129 -0.27784403 0.1023216 0.4720979 0
#> 130 -0.27784403 0.1023216 0.4720979 0
#> 131 -0.27784403 0.1023216 0.4720979 0
#> 132 -0.27784403 0.1023216 0.4720979 0
#> 133 -0.27784403 0.1023216 0.4720979 0
#> 134 -0.27784403 0.1023216 0.4720979 0
#> 135 -0.27784403 0.1023216 0.4720979 0
#> 136 -0.27784403 0.1023216 0.4720979 0
#> 137 -0.27784403 0.1023216 0.4720979 0
#> 138 -0.27784403 0.1023216 0.4720979 0
#> 139 -0.27784403 0.1023216 0.4720979 0
#> 140 -0.27784403 0.1023216 0.4720979 0
#> 141 -0.27784403 0.1023216 0.4720979 0
#> 142 -0.27784403 0.1023216 0.4720979 0
#> 143 -0.27784403 0.1023216 0.4720979 0
#> 144 -0.27784403 0.1023216 0.4720979 0
#> 145 -0.27784403 0.1023216 0.4720979 0
#> 146 -0.27784403 0.1023216 0.4720979 0
#> 147 -0.27784403 0.1023216 0.4720979 0
#> 148 -0.27784403 0.1023216 0.4720979 0
#> 149 -0.27784403 0.1023216 0.4720979 0
#> 150 -0.27784403 0.1023216 0.4720979 0
#> 151 -0.27784403 0.1023216 0.4720979 0
#> 152 -0.27784403 0.1023216 0.4720979 0
#> 153 -0.27784403 0.1023216 0.4720979 0
#> 154 -0.27784403 0.1023216 0.4720979 0
#> 155 -0.27784403 0.1023216 0.4720979 0
#> 156 -0.27784403 0.1023216 0.4720979 0
#> 157 -0.27784403 0.1023216 0.4720979 0
#> 158 -0.27784403 0.1023216 0.4720979 0
#> 159 -0.27784403 0.1023216 0.4720979 0
#> 160 -0.27784403 0.1023216 0.4720979 0
#> 161 -0.27784403 0.1023216 0.4720979 0
#> 162 0.12871903 0.5184149 0.9892527 1
#> 163 0.12871903 0.5184149 0.9892527 1
#> 164 0.12871903 0.5184149 0.9892527 1
#> 165 0.12871903 0.5184149 0.9892527 1
#> 166 0.12871903 0.5184149 0.9892527 1
#> 167 0.12871903 0.5184149 0.9892527 1
#> 168 0.12871903 0.5184149 0.9892527 1
#> 169 0.12871903 0.5184149 0.9892527 1
#> 170 0.12871903 0.5184149 0.9892527 1
#> 171 0.12871903 0.5184149 0.9892527 1
#> 172 0.12871903 0.5184149 0.9892527 1
#> 173 0.12871903 0.5184149 0.9892527 1
#> 174 0.12871903 0.5184149 0.9892527 1
#> 175 0.12871903 0.5184149 0.9892527 1
#> 176 0.12871903 0.5184149 0.9892527 1
#> 177 0.12871903 0.5184149 0.9892527 1
#> 178 0.12871903 0.5184149 0.9892527 1
#> 179 0.12871903 0.5184149 0.9892527 1
#> 180 0.12871903 0.5184149 0.9892527 1
#> 181 0.12871903 0.5184149 0.9892527 1
#> 182 0.12871903 0.5184149 0.9892527 1
#> 183 0.12871903 0.5184149 0.9892527 1
#> 184 0.12871903 0.5184149 0.9892527 1
#> 185 0.12871903 0.5184149 0.9892527 1
#> 186 0.12871903 0.5184149 0.9892527 1
#> 187 0.12871903 0.5184149 0.9892527 1
#> 188 0.12871903 0.5184149 0.9892527 1
#> 189 0.12871903 0.5184149 0.9892527 1
#> 190 0.12871903 0.5184149 0.9892527 1
#> 191 0.12871903 0.5184149 0.9892527 1
#> 192 0.12871903 0.5184149 0.9892527 1
#> 193 0.12871903 0.5184149 0.9892527 1
#> 194 0.12871903 0.5184149 0.9892527 1
#> 195 0.12871903 0.5184149 0.9892527 1
#> 196 0.12871903 0.5184149 0.9892527 1
#> 197 0.12871903 0.5184149 0.9892527 1
#> 198 0.12871903 0.5184149 0.9892527 1
#> 199 0.12871903 0.5184149 0.9892527 1
#> 200 0.12871903 0.5184149 0.9892527 1
#> 201 0.12871903 0.5184149 0.9892527 1
#> 202 0.12871903 0.5184149 0.9892527 1
#> 203 0.12871903 0.5184149 0.9892527 1
#> 204 0.12871903 0.5184149 0.9892527 1
#> 205 0.12871903 0.5184149 0.9892527 1
#> 206 0.12871903 0.5184149 0.9892527 1
#> 207 0.12871903 0.5184149 0.9892527 1
#> 208 0.12871903 0.5184149 0.9892527 1
#> 209 0.12871903 0.5184149 0.9892527 1
#> 210 0.12871903 0.5184149 0.9892527 1
#> 211 0.12871903 0.5184149 0.9892527 1
#> 212 0.12871903 0.5184149 0.9892527 1
#> 213 0.12871903 0.5184149 0.9892527 1
#> 214 0.12871903 0.5184149 0.9892527 1
#> 215 0.12871903 0.5184149 0.9892527 1
#> 216 0.12871903 0.5184149 0.9892527 1
#> 217 0.12871903 0.5184149 0.9892527 1
#> 218 0.12871903 0.5184149 0.9892527 1
#> 219 0.12871903 0.5184149 0.9892527 1
#> 220 0.12871903 0.5184149 0.9892527 1
#> 221 0.12871903 0.5184149 0.9892527 1
#> 222 0.12871903 0.5184149 0.9892527 1
#> 223 0.12871903 0.5184149 0.9892527 1
#> 224 0.12871903 0.5184149 0.9892527 1
#> 225 0.12871903 0.5184149 0.9892527 1
#> 226 0.12871903 0.5184149 0.9892527 1
#> 227 0.12871903 0.5184149 0.9892527 1
#> 228 0.12871903 0.5184149 0.9892527 1
#> 229 0.12871903 0.5184149 0.9892527 1
#> 230 0.12871903 0.5184149 0.9892527 1
#> 231 0.12871903 0.5184149 0.9892527 1
#> 232 0.12871903 0.5184149 0.9892527 1
#> 233 0.12871903 0.5184149 0.9892527 1
#> 234 0.12871903 0.5184149 0.9892527 1
#> 235 0.12871903 0.5184149 0.9892527 1
#> 236 0.12871903 0.5184149 0.9892527 1
#> 237 0.12871903 0.5184149 0.9892527 1
#> 238 0.12871903 0.5184149 0.9892527 1
#> 239 0.12871903 0.5184149 0.9892527 1
#> 240 0.12871903 0.5184149 0.9892527 1
#> 241 0.12871903 0.5184149 0.9892527 1
#> 242 0.12871903 0.5184149 0.9892527 1decision_go = 1 indicates Go, and
decision_go = 0 indicates No-Go under the
criterion
.