mixAK (version 2.2)

NMixPredCDFMarg: 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, ...)
## S3 method for class 'default':
NMixPredCDFMarg(x, scale, K, w, mu, Li, Krandom=TRUE, \dots)
## S3 method for class 'NMixMCMC':
NMixPredCDFMarg(x, grid, lgrid=50, scaled=FALSE, \dots)
## S3 method for class 'GLMM_MCMC':
NMixPredCDFMarg(x, grid, lgrid=50, scaled=FALSE, \dots)

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

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

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 cdf's.
• 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 cdf 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.

References

$\mbox{Kom\'{a}rek, A.}$ 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.