NMixPredCDFMarg {mixAK} | R Documentation

## Marginal (univariate) predictive cumulative distribution function

### Description

This function serves as an inference tool for the MCMC output obtained using the function `NMixMCMC`. It computes estimated posterior predictive cumulative distribution function for each margin.

### Usage

    NMixPredCDFMarg(x, ...)

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

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

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

### Arguments

`x` | an object of class `NMixMCMC` for `NMixPredCDFMarg.NMixMCMC` function. An object of class `GLMM_MCMC` for `NMixPredCDFMarg.GLMM_MCMC` function. A list with the grid values (see below) for `NMixPredCDFMarg.default` function.
`scale` | a two component list giving the `shift` and the `scale`.
`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 numeric vector or a list with the grid values in which the predictive CDF 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`.
`lgrid` | a length of the grid used to create the `grid` if that is not specified.
`scaled` | if `TRUE`, the CDF 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 `NMixPredCDFMarg` 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.
`cdf` | a list with the computed predictive CDF's for each margin. The components of the list are named `1`, ..., i.e., `cdf[[1]]`=`cdf[["1"]]` is the predictive density for margin 1 etc.
`cdfK` | a list with the computed predictive CDF's for each margin, conditioned further by K. The components of the list are named `1`, .... That is, `cdf[[1]][[1]]` = `cdf[["1"]][[1]]` is the predictive CDF for margin 1 conditioned by K=1, `cdf[[1]][[2]]` = `cdf[["1"]][[2]]` is the predictive CDF for margin 1 conditioned by K=2 etc. Note that `cdfK` 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. 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, 3932–3947.

### See Also

`plot.NMixPredCDFMarg`, `NMixMCMC`, `GLMM_MCMC`.

[Package mixAK version 0.6 Index]