Approximate posterior with (log) normal distribution

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 class '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,
  ...
)

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)

Value

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

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)
  )
)
#> Used default patient IDs!

# 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
  )
)
#> Emini is: 0.1148856025
#> xmini are:
#> -0.1505443075 0.05993339725 0.7553012661 0.475579731 0.04991811724 
#> Totally it used 0.000104 secs
#> No. of function call is: 1
#> Algorithm reached max number of iterations.

posterior <- approximation$model

# nolint end