approximate  R Documentation 
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 lognormal 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
approximate(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))
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