tidy: Tidying 'CrmPackClass' objects

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

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 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.

Usage

tidy(x, ...)

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

## S4 method for signature 'GeneralData'
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 'Simulations'
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 'Samples'
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 tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

The tibble object.

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()
.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()
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()

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.