mixAK: NMixPredDensJoint2 – R documentation

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NMixPredDensJoint2

Pairwise bivariate predictive density

Description

This function serves as an inference tool for the MCMC output obtained using the function NMixMCMC. It computes estimated posterior predictive densities for each pair of margins.

Usage

NMixPredDensJoint2(x, ...)

## Default S3 method:
NMixPredDensJoint2(x, scale, K, w, mu, Li, Krandom=TRUE, ...)

## S3 method for class 'NMixMCMC'
NMixPredDensJoint2(x, grid, lgrid=50, scaled=FALSE, ...)

## S3 method for class 'GLMM_MCMC'
NMixPredDensJoint2(x, grid, lgrid=50, scaled=FALSE, ...)

Arguments

`x`	an object of class `NMixMCMC` for `NMixPredDensJoint2.NMixMCMC` function. an object of class `GLMM_MCMC` for `NMixPredDensJoint2.GLMM_MCMC` function. A list with the grid values (see below) for `NMixPredDensJoint2.default` function.
`scale`	a two component list giving the `shift` and the `scale`. If not given, shift is equal to zero and scale is equal to one.
`K`	either a number (when `Krandom`=`FALSE`) or a numeric vector with the chain for the number of mixture components.
`w`	a numeric vector with the chain for the mixture weights.
`mu`	a numeric vector with the chain for the mixture means.
`Li`	a numeric vector with the chain for the mixture inverse variances (lower triangles only).
`Krandom`	a logical value which indicates whether the number of mixture components changes from one iteration to another.
`grid`	a list with the grid values for each margin in which the predictive density should be evaluated. If `grid` is not specified, it is created automatically using the information from the posterior summary statistics stored in `x`.
`lgrid`	a length of the grid used to create the `grid` if that is not specified.
`scaled`	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.

Value

An object of class NMixPredDensJoint2 which has the following components:

`x`	a list with the grid values for each margin. The components of the list are named `x1`, ... or take names from `grid` argument.
`freqK`	frequency table for the values of K (numbers of mixture components) in the MCMC chain.
`propK`	proportions derived from `freqK`.
`MCMC.length`	the length of the MCMC used to compute the predictive densities.
`dens`	a list with the computed predictive densities for each pair of margins. The components of the list are named `1-2`, `1-3`, ..., i.e., `dens[[1]]`=`dens[["1-2"]]` is the pairwise predictive density for margins 1 and 2, etc. Each component of the `list` is a matrix in such a form that it can be directly passed together with the proper components of `x` to the plotting functions like `contour` or `image`.
`densK`	a list with the computed predictive densities for each margin, conditioned further by K. The components of the list are named `1-2`, `1-3`, .... That is, `dens[[1]][[1]]` = `dens[["1-2"]][[1]]` is the pairwise predictive density for margins 1 and 2 conditioned by K=1, `dens[[1]][[2]]` = `dens[["1-2"]][[2]]` is the pairwise predictive density for margins 1 and 2 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.

Author(s)

Arnošt Komárek arnost.komarek[AT]mff.cuni.cz

References

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.

Examples

## See additional material available in 
## YOUR_R_DIR/library/mixAK/doc/
## or YOUR_R_DIR/site-library/mixAK/doc/
## http://www.karlin.mff.cuni.cz/~komarek/software/mixAK/Galaxy.pdf
## http://www.karlin.mff.cuni.cz/~komarek/software/mixAK/Faithful.pdf
## http://www.karlin.mff.cuni.cz/~komarek/software/mixAK/Tandmob.pdf
##

mixAK

Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

v5.3

GPL (>= 3)

Authors

Arnošt Komárek <arnost.komarek@mff.cuni.cz>

Initial release

2020-06-02