NMixMCMC
. It computes
(posterior predictive) estimates of univariate conditional densities.NMixPredCondDensMarg(x, ...)## S3 method for class 'default':
NMixPredCondDensMarg(x, icond, prob, scale, K, w, mu, Li, Krandom=FALSE, ...)
## S3 method for class 'NMixMCMC':
NMixPredCondDensMarg(x, icond, prob, grid, lgrid=50, scaled=FALSE, \dots)
## S3 method for class 'GLMM_MCMC':
NMixPredCondDensMarg(x, icond, prob, grid, lgrid=50, scaled=FALSE, \dots)
NMixMCMC
for
NMixPredCondDensMarg.NMixMCMC
function. An object of class GLMM_MCMC
for
NMixPredCondDensMarg.GLMM_MCMC
function.
A list with the grid values (see be
prob
. These can be used to draw
pointwise credible intervals.shift
and the
scale
. If not given, shift is equal to zero and scale is
equal to one.Krandom
$=$FALSE
) or a
numeric vector with the chain for the number of mixture components.grid[[icond]]
determines the values by which we condition. If grid
is not specified, it is created automatically usin
grid
if
that is not specified.TRUE
, the density of shifted and scaled data is
summarized. The shift and scale vector are taken from the
scale
component of the object x
.NMixPredCondDensMarg
which has the following components:x1
, ...or take names from
grid
argument.x[[icond]]
. Each dens[[j]]
is again a list
with conditional densities for each margin given margin
icond
equal to x[[icond]][j]
.
The value of dens[[j]][[imargin]]
gives a value
of a marginal density of the imargin
-th margin at x[[icond]][j]
.prob
.prob
is given then there is one
additional component named prob
which has the same structure as the
component dens
and keeps computed posterior pointwise
quantiles.plot
method implemented for the resulting object.plot.NMixPredCondDensMarg
, NMixMCMC
, GLMM_MCMC
.