examples/Model-method-prob.R

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Posterior for Prob(DLT | dose = 50).
prob(dose = 50, model = my_model, samples = my_samples)

# Create data from the 'DataDual' class.
data_dual <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data_dual
)

# Get samples from posterior.
dlt_sample <- mcmc(data = data_dual, model = dlt_model, options = my_options)

# Posterior for Prob(DLT | dose = 100).
prob(dose = 100, model = dlt_model, samples = dlt_sample)
prob(dose = c(50, 150), model = dlt_model)