Tidying CrmPackClass objects
Source: R/CrmPackClass-methods.R, R/Data-methods.R, R/Simulations-class.R, and 5 more
tidy.Rd![[Experimental]](figures/lifecycle-experimental.svg)
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 class 'CrmPackClass'
tidy(x, ...)
# S4 method for class 'GeneralData'
tidy(x, ...)
# S4 method for class 'Data'
tidy(x, ...)
# S4 method for class 'DataGrouped'
tidy(x, ...)
# S4 method for class 'DataDA'
tidy(x, ...)
# S4 method for class 'DataDual'
tidy(x, ...)
# S4 method for class 'DataParts'
tidy(x, ...)
# S4 method for class 'DataMixture'
tidy(x, ...)
# S4 method for class 'DataOrdinal'
tidy(x, ...)
# S4 method for class 'DataCombo'
tidy(x, ...)
# S4 method for class 'HierarchicalData'
tidy(x, ...)
# S4 method for class 'Simulations'
tidy(x, ...)
# S4 method for class 'ComboSimulations'
tidy(x, ...)
# S4 method for class 'HierarchicalSimulations'
tidy(x, ...)
# S4 method for class 'HierarchicalModel'
tidy(x, ...)
# S4 method for class 'LogisticIndepBeta'
tidy(x, ...)
# S4 method for class 'Effloglog'
tidy(x, ...)
# S4 method for class 'IncrementsMaxToxProb'
tidy(x, ...)
# S4 method for class 'IncrementsRelative'
tidy(x, ...)
# S4 method for class 'CohortSizeDLT'
tidy(x, ...)
# S4 method for class 'CohortSizeMin'
tidy(x, ...)
# S4 method for class 'CohortSizeMax'
tidy(x, ...)
# S4 method for class 'CohortSizeRange'
tidy(x, ...)
# S4 method for class 'CohortSizeParts'
tidy(x, ...)
# S4 method for class 'IncrementsMin'
tidy(x, ...)
# S4 method for class 'IncrementsRelative'
tidy(x, ...)
# S4 method for class 'IncrementsRelativeDLT'
tidy(x, ...)
# S4 method for class 'IncrementsRelativeParts'
tidy(x, ...)
# S4 method for class 'NextBestNCRM'
tidy(x, ...)
# S4 method for class 'NextBestNCRMLoss'
tidy(x, ...)
# S4 method for class 'DualDesign'
tidy(x, ...)
# S4 method for class 'DesignCombo'
tidy(x, ...)
# S4 method for class 'HierarchicalDesign'
tidy(x, ...)
# S4 method for class 'ArmConditionList'
tidy(x, ...)
# S4 method for class 'DesignArm'
tidy(x, ...)
# S4 method for class 'Samples'
tidy(x, ...)
# S4 method for class '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.
Examples
CohortSizeConst(3) %>% tidy()
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 3
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
# 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)
)
#> Used default patient IDs!
# Tidy the Data object
tidied_data <- tidy(sample_data)
# Print the tidied data
print(tidied_data)
#> # A tibble: 8 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> 0
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> 0
#> 3 3 3 1.5 3 FALSE FALSE 8 41 <dbl [41]> 0
#> 4 4 4 3 4 FALSE FALSE 8 41 <dbl [41]> 0
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> 0
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> 1
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultSimulations() %>% tidy()
#> $fit
#> $fit[[1]]
#> middle lower upper
#> 1 0.01782168 2.429295e-05 0.1227684
#> 2 0.03912615 4.842415e-04 0.1989020
#> 3 0.05938077 2.070762e-03 0.2317438
#> 4 0.11065415 1.190561e-02 0.2991191
#> 5 0.16329586 3.330604e-02 0.3787478
#> 6 0.21612952 6.734805e-02 0.4231658
#> 7 0.26771983 1.018562e-01 0.4853480
#> 8 0.40457531 2.008041e-01 0.6484751
#> 9 0.47689653 2.423075e-01 0.7487945
#> 10 0.62075004 3.364255e-01 0.8996310
#> 11 0.67898122 3.690798e-01 0.9421330
#>
#>
#> $stop_report
#> # A tibble: 1 × 1
#> stop_report[,NA] [,NA] [,"≥ 3 cohorts dosed"] [,"P(0.2 ≤ prob(DLE | NBD) ≤ 0…¹
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE TRUE TRUE TRUE
#> # ℹ abbreviated name: ¹[,"P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5"]
#> # ℹ 1 more variable: stop_report[5] <lgl>
#>
#> $data
#> $data[[1]]
#> # A tibble: 17 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 3 2 FALSE FALSE 17 11 <dbl [11]> 1
#> 2 2 2 5 3 FALSE FALSE 17 11 <dbl [11]> 1
#> 3 3 3 10 4 FALSE FALSE 17 11 <dbl [11]> 1
#> 4 4 4 20 6 FALSE FALSE 17 11 <dbl [11]> 1
#> 5 5 5 25 7 TRUE FALSE 17 11 <dbl [11]> 1
#> 6 6 6 25 7 FALSE FALSE 17 11 <dbl [11]> 1
#> 7 7 6 25 7 TRUE FALSE 17 11 <dbl [11]> 1
#> 8 8 6 25 7 FALSE FALSE 17 11 <dbl [11]> 1
#> 9 9 7 25 7 TRUE FALSE 17 11 <dbl [11]> 1
#> 10 10 7 25 7 TRUE FALSE 17 11 <dbl [11]> 1
#> 11 11 7 25 7 FALSE FALSE 17 11 <dbl [11]> 1
#> 12 12 8 15 5 FALSE FALSE 17 11 <dbl [11]> 1
#> 13 13 8 15 5 FALSE FALSE 17 11 <dbl [11]> 1
#> 14 14 8 15 5 FALSE FALSE 17 11 <dbl [11]> 1
#> 15 15 9 20 6 FALSE FALSE 17 11 <dbl [11]> 1
#> 16 16 9 20 6 FALSE FALSE 17 11 <dbl [11]> 1
#> 17 17 9 20 6 FALSE FALSE 17 11 <dbl [11]> 1
#> # ℹ 1 more variable: Backfilled <lgl>
#>
#>
#> $doses
#> # A tibble: 1 × 1
#> doses
#> <dbl>
#> 1 25
#>
#> $seed
#> # A tibble: 1 × 1
#> seed
#> <int>
#> 1 819
#>
#> attr(,"class")
#> [1] "tbl_Simulations" "list"
.DefaultLogisticIndepBeta() %>% tidy()
#> $pseudoData
#> # A tibble: 2 × 3
#> Dose N Tox
#> <dbl> <int> <dbl>
#> 1 25 3 1.05
#> 2 300 3 1.8
#>
#> $data
#> # A tibble: 0 × 11
#> # ℹ 11 variables: ID <int>, Cohort <int>, Dose <dbl>, XLevel <int>, Tox <lgl>,
#> # Placebo <lgl>, NObs <int>, NGrid <int>, DoseGrid <list>, Response <int>,
#> # Backfilled <lgl>
#>
#> $params
#> # A tibble: 2 × 3
#> Param mean cov
#> <chr> <dbl> <named list>
#> 1 Phi1 -1.95 <dbl [2 × 2]>
#> 2 Phi2 0.412 <dbl [2 × 2]>
#>
#> attr(,"class")
#> [1] "tbl_LogisticIndepBeta" "list"
.DefaultEffloglog() %>% tidy()
#> $pseudoData
#> # A tibble: 2 × 2
#> Dose Response
#> <dbl> <dbl>
#> 1 25 1.22
#> 2 300 2.51
#>
#> $data
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 25 1 FALSE FALSE 8 12 <dbl [12]> NA
#> 2 2 2 50 2 FALSE FALSE 8 12 <dbl [12]> NA
#> 3 3 2 50 2 FALSE FALSE 8 12 <dbl [12]> NA
#> 4 4 3 75 3 FALSE FALSE 8 12 <dbl [12]> NA
#> 5 5 4 100 4 TRUE FALSE 8 12 <dbl [12]> NA
#> 6 6 4 100 4 TRUE FALSE 8 12 <dbl [12]> NA
#> 7 7 5 225 9 TRUE FALSE 8 12 <dbl [12]> NA
#> 8 8 6 300 12 TRUE FALSE 8 12 <dbl [12]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, W <dbl>
#>
#> $params
#> # A tibble: 2 × 3
#> Param mean cov
#> <chr> <dbl> <named list>
#> 1 theta1 -2.82 <dbl [2 × 2]>
#> 2 theta2 2.71 <dbl [2 × 2]>
#>
#> attr(,"class")
#> [1] "tbl_Effloglog" "list"
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05)) %>% tidy()
#> # A tibble: 2 × 2
#> Grade Prob
#> <chr> <dbl>
#> 1 DLAE 0.2
#> 2 CRS 0.05
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 20 1
#> 2 20 Inf 3
.DefaultCohortSizeDLT() %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
.DefaultCohortSizeMin() %>% tidy()
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 10 1
#> 2 10 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMin" "tbl_CohortSizeMin" "list"
.DefaultCohortSizeMax() %>% tidy()
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 10 1
#> 2 10 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMax" "tbl_CohortSizeMax" "list"
.DefaultCohortSizeRange() %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 30 1
#> 2 30 Inf 3
CohortSizeParts(cohort_sizes = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 2
#> part cohort_size
#> <int> <int>
#> 1 1 1
#> 2 2 3
.DefaultIncrementsMin() %>% tidy()
#> [[1]]
#> # A tibble: 3 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 1 1
#> 2 1 3 0.33
#> 3 3 Inf 0.2
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 20 1
#> 2 20 Inf 0.33
#>
#> attr(,"class")
#> [1] "tbl_IncrementsMin" "tbl_IncrementsMin" "list"
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 20 1
#> 2 20 Inf 3
x <- .DefaultIncrementsRelativeDLT()
x %>% tidy()
#> # A tibble: 3 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 1 1
#> 2 1 3 0.33
#> 3 3 Inf 0.2
.DefaultIncrementsRelativeParts() %>% tidy()
#> $dlt_start
#> # A tibble: 1 × 1
#> dlt_start
#> <int>
#> 1 0
#>
#> $clean_start
#> # A tibble: 1 × 1
#> clean_start
#> <int>
#> 1 1
#>
#> $intervals
#> # A tibble: 2 × 1
#> intervals
#> <dbl>
#> 1 0
#> 2 2
#>
#> $increments
#> # A tibble: 2 × 1
#> increments
#> <dbl>
#> 1 2
#> 2 1
#>
#> attr(,"class")
#> [1] "tbl_IncrementsRelativeParts" "list"
NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
) %>%
tidy()
#> # A tibble: 3 × 4
#> Range min max max_prob
#> <chr> <dbl> <dbl> <dbl>
#> 1 Underdose 0 0.2 NA
#> 2 Target 0.2 0.35 NA
#> 3 Overdose 0.35 1 0.25
.DefaultNextBestNCRMLoss() %>% tidy()
#> # A tibble: 4 × 5
#> Range Lower Upper LossCoefficient MaxOverdoseProb
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Underdose 0 0.2 1 0.25
#> 2 Target 0.2 0.35 0 0.25
#> 3 Overdose 0.35 0.6 1 0.25
#> 4 Unacceptable 0.6 1 2 0.25
.DefaultDualDesign() %>% tidy()
#> $model
#> $sigma2betaW
#> # A tibble: 1 × 1
#> sigma2betaW
#> <dbl>
#> 1 0.01
#>
#> $rw1
#> # A tibble: 1 × 1
#> rw1
#> <lgl>
#> 1 TRUE
#>
#> $betaZ_params
#> # A tibble: 2 × 3
#> mean cov[,1] [,2] prec[,1] [,2]
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 0 1 0
#> 2 1 0 1 0 1
#>
#> $ref_dose
#> # A tibble: 1 × 1
#> ref_dose
#> <pstv_nmb>
#> 1 1
#>
#> $use_log_dose
#> # A tibble: 1 × 1
#> use_log_dose
#> <lgl>
#> 1 FALSE
#>
#> $sigma2W
#> # A tibble: 2 × 1
#> sigma2W
#> <dbl>
#> 1 0.1
#> 2 0.1
#>
#> $rho
#> # A tibble: 2 × 1
#> rho
#> <dbl>
#> 1 1
#> 2 1
#>
#> $use_fixed
#> # A tibble: 3 × 1
#> use_fixed
#> <lgl>
#> 1 FALSE
#> 2 FALSE
#> 3 TRUE
#>
#> $datanames
#> # A tibble: 5 × 1
#> datanames
#> <chr>
#> 1 nObs
#> 2 w
#> 3 x
#> 4 xLevel
#> 5 y
#>
#> $datanames_prior
#> # A tibble: 2 × 1
#> datanames_prior
#> <chr>
#> 1 nGrid
#> 2 doseGrid
#>
#> $sample
#> # A tibble: 5 × 1
#> sample
#> <chr>
#> 1 betaZ
#> 2 precW
#> 3 rho
#> 4 betaW
#> 5 delta
#>
#> attr(,"class")
#> [1] "tbl_DualEndpointRW" "list"
#>
#> $data
#> # A tibble: 0 × 12
#> # ℹ 12 variables: ID <int>, Cohort <int>, Dose <dbl>, XLevel <int>, Tox <lgl>,
#> # Placebo <lgl>, NObs <int>, NGrid <int>, DoseGrid <list>, Response <int>,
#> # Backfilled <lgl>, W <dbl>
#>
#> $stopping
#> $stop_list
#> $stop_list[[1]]
#> $target
#> # A tibble: 2 × 1
#> target
#> <dbl>
#> 1 0.9
#> 2 1
#>
#> $is_relative
#> # A tibble: 1 × 1
#> is_relative
#> <lgl>
#> 1 TRUE
#>
#> $prob
#> # A tibble: 1 × 1
#> prob
#> <dbl>
#> 1 0.5
#>
#> $report_label
#> # A tibble: 1 × 1
#> report_label
#> <chr>
#> 1 P(0.9 ≤ Biomarker ≤ 1) ≥ 0.5 (relative)
#>
#> attr(,"class")
#> [1] "tbl_StoppingTargetBiomarker" "list"
#>
#> $stop_list[[2]]
#> # A tibble: 1 × 2
#> nPatients report_label
#> <int> <chr>
#> 1 40 ≥ 40 patients dosed
#>
#>
#> $report_label
#> # A tibble: 1 × 1
#> report_label
#> <chr>
#> 1 NA
#>
#> attr(,"class")
#> [1] "tbl_StoppingAny" "list"
#>
#> $increments
#> # A tibble: 2 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 20 1
#> 2 20 Inf 0.33
#>
#> $pl_cohort_size
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 0
#>
#> $backfill
#> $cohort_size
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 3
#>
#> $opening
#> list()
#> attr(,"class")
#> [1] "tbl_OpeningNone" "list"
#>
#> $recruitment
#> list()
#> attr(,"class")
#> [1] "tbl_RecruitmentUnlimited" "list"
#>
#> $max_size
#> # A tibble: 1 × 1
#> max_size
#> <int>
#> 1 1000000
#>
#> $priority
#> # A tibble: 1 × 1
#> priority
#> <chr>
#> 1 highest
#>
#> attr(,"class")
#> [1] "tbl_Backfill" "list"
#>
#> $nextBest
#> $target
#> # A tibble: 2 × 1
#> target
#> <dbl>
#> 1 0.9
#> 2 1
#>
#> $overdose
#> # A tibble: 2 × 1
#> overdose
#> <dbl>
#> 1 0.35
#> 2 1
#>
#> $max_overdose_prob
#> # A tibble: 1 × 1
#> max_overdose_prob
#> <dbl>
#> 1 0.25
#>
#> $target_relative
#> # A tibble: 1 × 1
#> target_relative
#> <lgl>
#> 1 TRUE
#>
#> $target_thresh
#> # A tibble: 1 × 1
#> target_thresh
#> <dbl>
#> 1 0.01
#>
#> attr(,"class")
#> [1] "tbl_NextBestDualEndpoint" "list"
#>
#> $cohort_size
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 30 1
#> 2 30 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMax" "tbl_CohortSizeMax" "list"
#>
#> $startingDose
#> # A tibble: 1 × 1
#> startingDose
#> <dbl>
#> 1 3
#>
#> attr(,"class")
#> [1] "tbl_DualDesign" "list"
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()
#> $data
#> # A tibble: 2,000 × 10
#> Iteration Chain alpha0 alpha1 nChains nParameters nIterations nBurnin nThin
#> <int> <int> <dbl> <dbl> <int> <int> <int> <int> <int>
#> 1 1 1 0.249 0.647 1 1 2100 100 1
#> 2 2 1 0.169 1.01 1 1 2100 100 1
#> 3 3 1 -1.56 3.94 1 1 2100 100 1
#> 4 4 1 -1.19 4.51 1 1 2100 100 1
#> 5 5 1 -1.10 5.42 1 1 2100 100 1
#> 6 6 1 -2.95 1.92 1 1 2100 100 1
#> 7 7 1 0.155 0.971 1 1 2100 100 1
#> 8 8 1 -0.105 1.26 1 1 2100 100 1
#> 9 9 1 -0.780 1.26 1 1 2100 100 1
#> 10 10 1 -1.89 4.71 1 1 2100 100 1
#> # ℹ 1,990 more rows
#> # ℹ 1 more variable: parallel <lgl>
#>
#> $options
#> # A tibble: 1 × 5
#> iterations burnin step rng_kind rng_seed
#> <int> <int> <int> <chr> <int>
#> 1 2100 100 1 NA NA
#>
#> attr(,"class")
#> [1] "tbl_Samples" "list"