crmPack (version 2.0.0)

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 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 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 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 Simulations
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 Samples
tidy(x, ...)

Value

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

The tibble::tibble object.

The tibble::tibble object.

The tibble::tibble object.

The tibble::tibble object.

The tibble::tibble object.

The tibble::tibble object.

The tibble::tibble object.

The list of tibble::tibble objects.

The list of tibble::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()
.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()