size | R Documentation |
A method that computes the size of a given object. This can be for instance a size of a MCMC sample, or the size of a cohort. See the help of a specific method for more details.
size(object, ...)
## S4 method for signature 'McmcOptions'
size(object, ...)
## S4 method for signature 'CohortSizeRange'
size(object, dose, data)
## S4 method for signature 'CohortSizeDLT'
size(object, dose, data)
## S4 method for signature 'CohortSizeMax'
size(object, dose, data)
## S4 method for signature 'CohortSizeMin'
size(object, dose, data)
## S4 method for signature 'CohortSizeConst'
size(object, dose, ...)
## S4 method for signature 'CohortSizeParts'
size(object, dose, data)
## S4 method for signature 'CohortSizeOrdinal'
size(object, dose, data, ...)
## S4 method for signature 'Samples'
size(object, ...)
object |
( |
... |
not used. |
dose |
( |
data |
the data input, an object of class |
A size of a given object.
size(McmcOptions)
: compute the number of MCMC samples based on McmcOptions
object.
size(CohortSizeRange)
: Determines the size of the next cohort based on the range
into which the next dose falls into.
size(CohortSizeDLT)
: Determines the size of the next cohort based on the number
of DLTs so far.
size(CohortSizeMax)
: Determines the size of the next cohort based on maximum of
multiple cohort size rules.
size(CohortSizeMin)
: Determines the size of the next cohort based on minimum of
multiple cohort size rules.
size(CohortSizeConst)
: Constant cohort size.
size(CohortSizeParts)
: Determines the size of the next cohort based on the parts.
size(CohortSizeOrdinal)
: Determines the size of the next cohort in a ordinal CRM trial.
size(Samples)
: get the number of MCMC samples from Samples
object.
# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)
size(my_options)
# nolint start
# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
ref_dose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
max_overdose_prob=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Rule for the cohort size:
# - having cohort of size 1 for doses <10
# - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))
# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid =
c(
0.1, 0.5, 1.5, 3, 6,
seq(from = 10, to = 80, by = 2)
)
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov =
matrix(c(1, -0.5, -0.5, 1),
nrow = 2
),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements,
data = data
)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit = nextMaxDose,
samples = samples, model = model, data = data
)
# Rule for the cohort size:
# - having cohort of size 1 if no DLTs were yet observed
# - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(
intervals = c(0, 1),
cohort_size = c(1, 3)
)
# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid =
c(
0.1, 0.5, 1.5, 3, 6,
seq(from = 10, to = 80, by = 2)
)
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov =
matrix(c(1, -0.5, -0.5, 1),
nrow = 2
),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements,
data = data
)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit = nextMaxDose,
samples = samples, model = model, data = data
)
# Rule for having cohort of size 1 for doses <30
# and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
intervals = c(0, 10),
cohort_size = c(1, 3)
)
# Rule for having cohort of size 1 until no DLT were observed
# and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
intervals = c(0, 1),
cohort_size = c(1, 3)
)
# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)
# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid =
c(
0.1, 0.5, 1.5, 3, 6,
seq(from = 10, to = 80, by = 2)
)
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov =
matrix(c(1, -0.5, -0.5, 1),
nrow = 2
),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements,
data = data
)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit = nextMaxDose,
samples = samples, model = model, data = data
)
# Rule for having cohort of size 1 for doses <30
# and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
intervals = c(0, 30),
cohort_size = c(1, 3)
)
# Rule for having cohort of size 1 until no DLT were observed
# and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
intervals = c(0, 1),
cohort_size = c(1, 3)
)
# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)
# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
ref_dose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
max_overdose_prob=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size=3)
# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value)
# nolint end
# nolint start
# create an object of class 'DataParts'
data <- DataParts(
x = c(0.1, 0.5, 1.5),
y = c(0, 0, 0),
doseGrid = c(
0.1, 0.5, 1.5, 3, 6,
seq(from = 10, to = 80, by = 2)
),
part = c(1L, 1L, 1L),
nextPart = 1L,
part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov =
matrix(c(1, -0.5, -0.5, 1),
nrow = 2
),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)
myIncrements <- IncrementsRelativeParts(
dlt_start = 0,
clean_start = 1
)
nextMaxDose <- maxDose(myIncrements,
data = data
)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Rule for the cohort size:
mySize <- CohortSizeParts(cohort_sizes = c(1, 3))
# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)
# nolint end
CohortSizeOrdinal(
grade = 1L,
rule = CohortSizeRange(intervals = c(0L, 30L), cohort_size = c(1L, 3L))
)
# Set up the MCMC option in order to have a burn-in of 100 iterations and
# then take every other iteration up to a collection of 200 samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 200)
my_samples <- Samples(
data = list(alpha = rnorm(200), beta = rnorm(200)),
options = my_options
)
size(my_samples)
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