tidy: Tidying 'CrmPackClass' objects
In crmPack: Object-Oriented Implementation of Dose Escalation Designs

tidy	R Documentation

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 signature 'CrmPackClass'
tidy(x, ...)

## S4 method for signature 'GeneralData'
tidy(x, ...)

## S4 method for signature 'Data'
tidy(x, ...)

## S4 method for signature 'DataGrouped'
tidy(x, ...)

## S4 method for signature 'DataDA'
tidy(x, ...)

## S4 method for signature 'DataDual'
tidy(x, ...)

## S4 method for signature 'DataParts'
tidy(x, ...)

## S4 method for signature 'DataMixture'
tidy(x, ...)

## S4 method for signature 'DataOrdinal'
tidy(x, ...)

## S4 method for signature 'DataCombo'
tidy(x, ...)

## S4 method for signature 'HierarchicalData'
tidy(x, ...)

## S4 method for signature 'Simulations'
tidy(x, ...)

## S4 method for signature 'ComboSimulations'
tidy(x, ...)

## S4 method for signature 'HierarchicalSimulations'
tidy(x, ...)

## S4 method for signature 'HierarchicalModel'
tidy(x, ...)

## S4 method for signature 'LogisticIndepBeta'
tidy(x, ...)

## S4 method for signature 'Effloglog'
tidy(x, ...)

## S4 method for signature 'IncrementsMaxToxProb'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'CohortSizeDLT'
tidy(x, ...)

## S4 method for signature 'CohortSizeMin'
tidy(x, ...)

## S4 method for signature 'CohortSizeMax'
tidy(x, ...)

## S4 method for signature 'CohortSizeRange'
tidy(x, ...)

## S4 method for signature 'CohortSizeParts'
tidy(x, ...)

## S4 method for signature 'IncrementsMin'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeDLT'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeParts'
tidy(x, ...)

## S4 method for signature 'NextBestNCRM'
tidy(x, ...)

## S4 method for signature 'NextBestNCRMLoss'
tidy(x, ...)

## S4 method for signature 'DualDesign'
tidy(x, ...)

## S4 method for signature 'DesignCombo'
tidy(x, ...)

## S4 method for signature 'HierarchicalDesign'
tidy(x, ...)

## S4 method for signature 'ArmConditionList'
tidy(x, ...)

## S4 method for signature 'DesignArm'
tidy(x, ...)

## S4 method for signature 'Samples'
tidy(x, ...)

## S4 method for signature 'HierarchicalSamples'
tidy(x, ...)

Arguments

`x`	(`CrmPackClass`) the object to be tidied.
`...`	potentially used by class-specific methods.

Value

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

The tibble object.

The list of tibble objects.

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()

crmPack documentation built on July 5, 2026, 9:06 a.m.