crmPack (version 2.2.1)

tidy: Tidying CrmPackClass objects

Description

In the spirit of the broom package, provide a method to convert a CrmPackClass object to a (list of) tibbles.

Following the principles of the broom package, convert a CrmPackClass object to a (list of) tibbles. This is a basic, default representation.

A method that tidies a GeneralData object.

A method that tidies a Data object.

A method that tidies a DataGrouped object.

A method that tidies a DataDA object.

A method that tidies a DataDual object.

A method that tidies a DataParts object.

A method that tidies a DataMixture object.

A method that tidies a DataOrdinal object.

A method that tidies a DataCombo object.

A method that tidies a HierarchicalData object.

A method that tidies a HierarchicalModel object.

A method that tidies a LogisticIndepBeta object.

A method that tidies a Effloglog object.

Usage

tidy(x, ...)
# S4 method for CrmPackClass
tidy(x, ...)
# S4 method for GeneralData
tidy(x, ...)
# S4 method for Data
tidy(x, ...)
# S4 method for DataGrouped
tidy(x, ...)
# S4 method for DataDA
tidy(x, ...)
# S4 method for DataDual
tidy(x, ...)
# S4 method for DataParts
tidy(x, ...)
# S4 method for DataMixture
tidy(x, ...)
# S4 method for DataOrdinal
tidy(x, ...)
# S4 method for DataCombo
tidy(x, ...)
# S4 method for HierarchicalData
tidy(x, ...)
# S4 method for Simulations
tidy(x, ...)
# S4 method for ComboSimulations
tidy(x, ...)
# S4 method for HierarchicalSimulations
tidy(x, ...)
# S4 method for HierarchicalModel
tidy(x, ...)
# S4 method for LogisticIndepBeta
tidy(x, ...)
# S4 method for Effloglog
tidy(x, ...)
# S4 method for IncrementsMaxToxProb
tidy(x, ...)
# S4 method for IncrementsRelative
tidy(x, ...)
# S4 method for CohortSizeDLT
tidy(x, ...)
# S4 method for CohortSizeMin
tidy(x, ...)
# S4 method for CohortSizeMax
tidy(x, ...)
# S4 method for CohortSizeRange
tidy(x, ...)
# S4 method for CohortSizeParts
tidy(x, ...)
# S4 method for IncrementsMin
tidy(x, ...)
# S4 method for IncrementsRelative
tidy(x, ...)
# S4 method for IncrementsRelativeDLT
tidy(x, ...)
# S4 method for IncrementsRelativeParts
tidy(x, ...)
# S4 method for NextBestNCRM
tidy(x, ...)
# S4 method for NextBestNCRMLoss
tidy(x, ...)
# S4 method for DualDesign
tidy(x, ...)
# S4 method for DesignCombo
tidy(x, ...)
# S4 method for HierarchicalDesign
tidy(x, ...)
# S4 method for ArmConditionList
tidy(x, ...)
# S4 method for DesignArm
tidy(x, ...)
# S4 method for Samples
tidy(x, ...)
# S4 method for HierarchicalSamples
tidy(x, ...)

Value

A (list of) tibble(s) representing the object in tidy form.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The list of tibble objects.

The list of tibble objects.

The list of tibble objects.

Arguments

x

(CrmPackClass)
the object to be tidied.

...

potentially used by class-specific methods.

Usage Notes

The prior observations are indicated by a Cohort value of 0 in the returned tibble.

Examples

CohortSizeConst(3) %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
# Create a sample Data object
sample_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(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  response = c(0, 0, 0, 0, 0, 1, NA, NA),
  backfilled = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE)
)

# Tidy the Data object
tidied_data <- tidy(sample_data)

# Print the tidied data
print(tidied_data)
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultSimulations() %>% tidy()
.DefaultLogisticIndepBeta() %>% tidy()
.DefaultEffloglog() %>% tidy()
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05)) %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
.DefaultCohortSizeDLT() %>% tidy()
.DefaultCohortSizeMin() %>% tidy()
.DefaultCohortSizeMax() %>% tidy()
.DefaultCohortSizeRange() %>% tidy()
CohortSizeParts(cohort_sizes = c(1, 3)) %>% tidy()
.DefaultIncrementsMin() %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
x <- .DefaultIncrementsRelativeDLT()
x %>% tidy()
.DefaultIncrementsRelativeParts() %>% tidy()
NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
) %>%
  tidy()
.DefaultNextBestNCRMLoss() %>% tidy()
.DefaultDualDesign() %>% tidy()
options <- McmcOptions(
  burnin = 100,
  step = 1,
  samples = 2000
)

emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

samples <- mcmc(emptydata, model, options)
samples %>% tidy()