Approximate posterior with (log) normal distribution
It is recommended to use set.seed before, in order
to be able to reproduce the resulting approximating model exactly.
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, ...)
object |
the |
model |
the |
data |
the |
... |
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
|
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) |
the approximation model
Samples: Here the ... argument can transport additional arguments for
Quantiles2LogisticNormal, e.g. in order to control the
approximation quality, etc.
# 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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.