This function serves as an inference tool for the MCMC output
obtained using the function NMixMCMC. It computes
estimated posterior predictive densities for each margin.
NMixPredDensMarg(x, ...)# S3 method for default
NMixPredDensMarg(x, scale, K, w, mu, Li, Krandom=TRUE, ...)
# S3 method for NMixMCMC
NMixPredDensMarg(x, grid, lgrid=500, scaled=FALSE, ...)
# S3 method for GLMM_MCMC
NMixPredDensMarg(x, grid, lgrid=500, scaled=FALSE, ...)
An object of class NMixPredDensMarg which has the following components:
a list with the grid values for each margin. The components
of the list are named x1, ... or take names from
grid argument.
frequency table for the values of \(K\) (numbers of mixture components) in the MCMC chain.
proportions derived from freqK.
the length of the MCMC used to compute the predictive densities.
a list with the computed predictive densities for each
margin. The components of the list are named 1, ..., i.e.,
dens[[1]]\(=\)dens[["1"]] is the predictive
density for margin 1 etc.
a list with the computed predictive densities for each
margin, conditioned further by \(K\). The components of the list
are named 1, .... That is,
dens[[1]][[1]] \(=\) dens[["1"]][[1]] is the predictive
density for margin 1 conditioned by \(K=1\),
dens[[1]][[2]] \(=\) dens[["1"]][[2]] is the predictive
density for margin 1 conditioned by \(K=2\) etc.
Note that densK provides some additional information only
when Krandom \(=\) TRUE or when x results from
the NMixMCMC call to the reversible jump MCMC.
There is also a plot method implemented for the resulting object.
an object of class NMixMCMC for
NMixPredDensMarg.NMixMCMC function.
An object of class GLMM_MCMC for
NMixPredDensMarg.GLMM_MCMC function.
A list with the grid values (see below) for
NMixPredDensMarg.default function.
a two component list giving the shift and the scale.
either a number (when Krandom\(=\)FALSE) or a
numeric vector with the chain for the number of mixture components.
a numeric vector with the chain for the mixture weights.
a numeric vector with the chain for the mixture means.
a numeric vector with the chain for the mixture inverse variances (lower triangles only).
a logical value which indicates whether the number of mixture components changes from one iteration to another.
a numeric vector or a list with the grid values in which the predictive density should be evaluated.
If x$dim is 1 then grid may be a numeric vector. If
x$dim is higher than then grid must be a list with
numeric vectors as components giving the grids for each margin.
If grid is not specified, it is created automatically using
the information from the posterior summary statistics stored in x.
a length of the grid used to create the grid if
that is not specified.
if 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.
optional additional arguments.
Arnošt Komárek arnost.komarek@mff.cuni.cz
Komárek, A. (2009). A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data. Computational Statistics and Data Analysis, 53(12), 3932--3947.
plot.NMixPredDensMarg, NMixMCMC, GLMM_MCMC, NMixPredDensJoint2.
## See additional material available in
## YOUR_R_DIR/library/mixAK/doc/
## or YOUR_R_DIR/site-library/mixAK/doc/
## https://www2.karlin.mff.cuni.cz/~komarek/software/mixAK/Galaxy.pdf
## https://www2.karlin.mff.cuni.cz/~komarek/software/mixAK/Faithful.pdf
## https://www2.karlin.mff.cuni.cz/~komarek/software/mixAK/Tandmob.pdf
##
Run the code above in your browser using DataLab