crmPack (version 2.0.0)

approximate: Approximate posterior with (log) normal distribution

Description

To reproduce the resultant approximate model in the future exactly, include seed = xxxx in the call to approximate.

Usage

approximate(object, model, data, ...)
# S4 method for Samples
approximate(
  object,
  model,
  data,
  points = seq(from = min(data@doseGrid), to = max(data@doseGrid), length = 5L),
  refDose = median(points),
  logNormal = FALSE,
  verbose = TRUE,
  create_plot = TRUE,
  ...
)

Value

a list containing the approximation model and, if requested, a ggplot2 object containing a graphical representation of the fitted model

Arguments

object

the Samples object

model

the GeneralModel object

data

the Data object

...

additional arguments (see methods)

points

optional parameter, which gives the dose values at which the approximation should rely on (default: 5 values equally spaced from minimum to maximum of the dose grid)

refDose

the reference dose to be used (default: median of points)

logNormal

use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used

verbose

be verbose (progress statements)? (default)

create_plot

add a ggplot2 object to the return value (default)

Functions

• approximate(Samples): Here the ... argument can transport additional arguments for Quantiles2LogisticNormal, e.g. in order to control the approximation quality, etc.

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)

# Approximate the posterior distribution with a bivariate normal
# max.time and maxit are very small only for the purpose of showing the example. They
# should be increased for a real case.
set.seed(94)
approximation <- approximate(
  object = samples,
  model = model,
  data = data,
  logNormal = TRUE,
  control = list(
    threshold.stop = 0.1,
    max.time = 1,
    maxit = 1
  )
)

posterior <- approximation$model

# nolint end