approximate {crmPack}

Approximate posterior with (log) normal distribution

Description

It is recommended to use set.seed before, in order to be able to reproduce the resulting approximating model exactly.

Usage

approximate(object, model, data, ...)

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

Arguments

object

the Samples object

model

the Model 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 and plot)? (default)

Value

the approximation model

Functions

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

Examples

# 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),
                           refDose = 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)
posterior <- approximate(object = samples,
                         model = model,
                         data = data,
                         logNormal=TRUE,
                         control = list(threshold.stop = 0.1,
                                        max.time = 1,
                                        maxit = 1))