NMixMCMC. It computes
(posterior predictive) estimates of univariate conditional densities.
NMixPredCondDensMarg(x, ...)
"NMixPredCondDensMarg"(x, icond, prob, scale, K, w, mu, Li, Krandom=FALSE, ...)
"NMixPredCondDensMarg"(x, icond, prob, grid, lgrid=50, scaled=FALSE, ...)
"NMixPredCondDensMarg"(x, icond, prob, grid, lgrid=50, scaled=FALSE, ...)NMixMCMC for
NMixPredCondDensMarg.NMixMCMC function. An object of class GLMM_MCMC for
NMixPredCondDensMarg.GLMM_MCMC function.
A list with the grid values (see below) for
NMixPredCondDensMarg.default function.
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 using
the information from the posterior summary statistics stored in x.
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 qXX%, e.g., q50% for
each value of 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.