tidy | R Documentation |
CrmPackClass
objectsIn 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.
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 '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 'Samples'
tidy(x, ...)
x |
( |
... |
potentially used by class-specific methods. |
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 list
of tibble
objects.
The list
of tibble
objects.
The prior observations are indicated by a Cohort
value of 0
in the returned
tibble
.
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()
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