Summarizing Fits for Nonparametric Mixture Models with Conditionally Independent Multivariate Component Densities
summary method for class mvnpEM.
## S3 method for class 'mvnpEM' summary(object, ...) ## S3 method for class 'summary.mvnpEM' print(x, digits=3, ...)
object,x |
an object of class |
digits |
Significant digits for printing values |
... |
further arguments passed to or from other methods. |
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.
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 |
means |
A B x m matrix giving the estimated mean of each block in each component. |
variances |
Same as |
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
# 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)
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