crmPack (version 2.0.0)

fit: Fit method for the Samples class

Description

Note this new generic function is necessary because the fitted function only allows the first argument object to appear in the signature. But we need also other arguments in the signature.

Usage

fit(object, model, data, ...)
# S4 method for Samples,GeneralModel,Data
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
# S4 method for Samples,DualEndpoint,DataDual
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)
# S4 method for Samples,LogisticIndepBeta,Data
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
# S4 method for Samples,Effloglog,DataDual
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
# S4 method for Samples,EffFlexi,DataDual
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
# S4 method for Samples,LogisticLogNormalOrdinal,DataOrdinal
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Value

the data frame with required information (see method details)

Arguments

object

the Samples object

model

the GeneralModel object

data

the Data object

...

passed down to the prob() method.

points

at which dose levels is the fit requested? default is the dose grid

quantiles

the quantiles to be calculated (default: 0.025 and 0.975)

middle

the function for computing the middle point. Default: mean

Functions

• fit(object = Samples, model = GeneralModel, data = Data): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-toxicity curve

• fit(object = Samples, model = DualEndpoint, data = DataDual): This method returns a data frame with dose, and middle, lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix "Biomarker") curves, for all grid points (Note that currently only the grid points can be used, because the DualEndpointRW models only allow that)

• fit(object = Samples, model = LogisticIndepBeta, data = Data): This method return a data frame with dose, middle lower and upper quantiles for the dose-DLE curve using DLE samples for “LogisticIndepBeta” model class

• fit(object = Samples, model = Effloglog, data = DataDual): This method returns a data frame with dose, middle, lower, upper quantiles for the dose-efficacy curve using efficacy samples for “Effloglog” model class

• fit(object = Samples, model = EffFlexi, data = DataDual): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for “EffFlexi” model class

• fit(object = Samples, model = LogisticLogNormalOrdinal, data = DataOrdinal): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for the “LogisticLogNormalOrdinal” model class

Examples

# nolint start

# Create some 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 a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)


model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fitted <- fit(object = samples, model = model, data = data)

# nolint end
# nolint start

# Create some data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)

# nolint end
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fit(object = samples, model = model, data = data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)
# nolint start

##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)

# nolint end
model <- .DefaultLogisticLogNormalOrdinal()
ordinal_data <- .DefaultDataOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, model, options)

grade1_fit <- fit(samples, model, ordinal_data, grade = 1L)
grade2_fit <- fit(samples, model, ordinal_data, grade = 2L)