EMmixlcd | R Documentation |
Uses EM algorithm to estimate the mixture proportions and the component
densities. The output is an object of class "lcdmix"
which
contains mixture proportions at each observation and all the information of the
estimated component densities.
EMmixlcd( x, k = 2, y, props, epsratio=10^-6, max.iter=50,
epstheta=10^-8, verbose=-1 )
x |
Data in |
k |
The number of components, equals 2 by default |
y |
An |
props |
Vector of length |
epsratio |
EM algorithm will terminate if the increase in the proportion of the likelihood is
less than this specified ratio. Default value is |
max.iter |
The maximum number of iterations for the EM algorithm |
epstheta |
|
verbose |
|
An introduction to the Em algorithm can be found in McLachlan and Krishnan (1997).
Briefly, given the current estimates of the mixture proportions and component densities, we first
update the estimates of the mixture prroportions. We then update the estimates of the component
densities by using mlelcd
. In fact, the incorporation of the weights in the
maximization process in mlelcd
presents no additional complication.
In our case, because of the computational intensity of the method, we first cluster the points according to ta hierarchical Gaussian clustering model and then iterate the EM algorithm until the increase in the proportion of the likelihood is less than a pre-specified quantity at each step.
More technical details can be found in Cule, Samworth and Stewart(2010)
An object of class "lcdmix"
, with the following components:
x |
Data copied from input (may be reordered) |
logf |
An |
props |
Vector containing the estimated proportions of components |
niter |
Number of iterations of the EM algorithm |
lcdloglik |
The log-likelihood after the final iteration |
Yining Chen
Madeleine Cule
Robert B. Gramacy
Richard Samworth
Cule, M. L., Samworth, R. J., and Stewart, M. I. (2010) Maximum likelihood estimation of a log-concave density, Journal of the Royal Statistical Society, Series B, 72(5) p.545-607.
McLachlan, G. J. and Krishnan, T. (1997) The EM Algorithm and Extensions, New York: Wiley.
mclust
,
logcondens
,
plot.LogConcDEAD
,mlelcd
, dlcd
##Simple bivariate normal data
set.seed( 1 )
n = 15
d = 2
props=c( 0.6, 0.4 )
shift=2
x <- matrix( rnorm( n*d ), ncol = d )
shiftvec <- ifelse( runif( n ) > props[ 1 ], 0, shift )
x[,1] <- x[,1] + shiftvec
EMmixlcd( x, k = 2, max.iter = 2)
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