Posterior distribution over the type probability space of a LNRE
model, given the observed frequency \(m\) in a sample. Posterior
density (postdlnre) and log-transformed density
(postldlnre) can be computed for all LNRE models. The
distribution function (postplnre) and quantiles
(postqlnre) are only available for selected types of models.
postdlnre(model, x, m, N, …)
postldlnre(model, x, m, N, base=10, log.x=FALSE, …)
postplnre(model, q, m, N, lower.tail=FALSE, …)
postqlnre(model, p, m, N, lower.tail=FALSE, …)an object belonging to a subclass of lnre,
representing an LNRE model
frequency \(m\) of a type in the observed sample
sample size \(N\)
vector of type probabilities \(pi\) for which the posterior density function is evaluated
vector of type probability quantiles, i.e. threshold values \(\rho\) on the type probability axis
vector of tail probabilities
positive number, the base \(a\) with respect to which the log-transformation is peformed (see "Details" below)
if TRUE, the values passed in the argument
x are assumed to be logarithmic, i.e. \(\log_a \pi\)
if TRUE, lower tail probabilities or type
counts are returned / expected in the p argument. Note that
the defaults differ for distribution function and type distribution,
and see "Details" below.
further arguments are passed through to the method implementations (currently unused)
A vector of non-negative numbers of the same length as the second
argument (x, p or q).
postdlnre returns the posterior type density \(P(\pi | f = m)\)
for the values of \(\pi\) specified in the vector x.
postplnre computes the posterior type distribution function
\(P(\pi \geq \rho | f = m)\) (default) or its complement
\(P(\pi \leq \rho | f = m)\) (if lower.tail=TRUE).
These correspond to \(E[V_{m, >\rho}]\) and \(E[V_{m, \rho}]\), respectively (Evert 2004, p. 123).
postqlnre returns quantiles, i.e. the inverse of the posterior
type distribution function.
postldlnre computes a logarithmically transformed version of
the posterior type density, taking logarithms with respect to the
base \(a\) specified in the base argument (default: \(a=10\)).
Such log-transformed densities are useful for visualizing distributions,
see ldlnre for more information.
lnre for more information about LNRE models and how to
initialize them, LNRE for type density and distribution
functions (which represent the prior distribution).