R: Summarizing Fits for Nonparametric Mixture Models with...

summary.mvnpEM {mixtools} | R Documentation

## Summarizing Fits for Nonparametric Mixture Models with Conditionally Independent Multivariate Component Densities

### Description

`summary` method for class `mvnpEM`.

### Usage

    ## S3 method for class 'mvnpEM'
    summary(object, ...)
    ## S3 method for class 'summary.mvnpEM'
    print(x, digits=3, ...)

### Arguments

`object,x` | an object of class `mvnpEM` such as a result of a call to `mvnpEM`
`digits` | Significant digits for printing values
`...` | further arguments passed to or from other methods.

### Details

`summary.mvnpEM` prints means and variances of each block for each component. These quantities might not be part of the model, but they are estimated nonparametrically based on the posterior probabilities and the data.

### Value

The function `summary.mvnpEM` returns a list of type `summary.mvnpEM` with the following components:

`n` | The number of observations
`m` | The number of mixture components
`B` | The number of blocks
`blockid` | The block ID (from 1 through B) for each of the coordinates of the multivariate observations. The `blockid` component is of length r, the dimension of each observation.
`means` | A B x m matrix giving the estimated mean of each block in each component.
`variances` | Same as `means` but giving the estimated variances instead.

### References

Benaglia, T., Chauveau, D., and Hunter, D. R. (2009), An EM-like algorithm for semi- and non-parametric estimation in multivariate mixtures, Journal of Computational and Graphical Statistics, 18(2), 505–526.

Chauveau, D., and Hoang, V. T. L. (2015), Nonparametric mixture models with conditionally independent multivariate component densities, Preprint under revision. https://hal.archives-ouvertes.fr/hal-01094837

### See Also

`mvnpEM`, `plot.mvnpEM`

### Examples

    # Example as in Chauveau and Hoang (2015) with 6 coordinates
    ## Not run:
    m=2; r=6; blockid <-c(1,1,2,2,3,3) # 3 bivariate blocks
    # generate some data x ...
    a <- mvnpEM(x, mu0=2, blockid, samebw=F) # adaptive bandwidth
    plot(a) # this S3 method produces 6 plots of univariate marginals
    summary(a)
    ## End(Not run)

[Package mixtools version 1.2.0 Index]