multmixEM {mixtools} | R Documentation |
Return EM algorithm output for mixtures of multinomial distributions.
multmixEM(y, lambda = NULL, theta = NULL, k = 2, maxit = 10000, epsilon = 1e-08, verb = FALSE)
y |
An nxp matrix of data (multinomial counts), where n is the sample size and p is the number of multinomial bins. |
lambda |
Initial value of mixing proportions. Entries should sum to
1. This determines number of components. If NULL, then lambda is
random from uniform Dirichlet and number of
components is determined by theta . |
theta |
Initial value of theta parameters. Should be a kxp matrix,
where p is the number of columns of y and k is number of components.
Each row of theta should sum to 1.
If NULL, then each row is random from uniform Dirichlet.
If both lambda and theta are NULL, then number of components
is determined by k. |
k |
Number of components. Ignored unless lambda and theta
are NULL. |
epsilon |
The convergence criterion. |
maxit |
The maximum number of iterations. |
verb |
If TRUE, then various updates are printed during each iteration of the algorithm. |
multmixEM
returns a list of class mixEM
with items:
y |
The raw data. |
lambda |
The final mixing proportions. |
theta |
The final multinomial parameters. |
loglik |
The final log-likelihood. |
posterior |
An nxk matrix of posterior probabilities for observations. |
all.loglik |
A vector of each iteration's log-likelihood. |
restarts |
The number of times the algorithm restarted due to unacceptable choice of initial values. |
ft |
A character vector giving the name of the function. |
McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley & Sons, Inc.
Elmore, R. T., Hettmansperger, T. P. and Xuan, F. (2004) The Sign Statistic, One-Way Layouts and Mixture Models, Statistical Science 19(4), 579–587.
compCDF
, makemultdata
, multmixmodel.sel
## The sulfur content of the coal seams in Texas A<-c(1.51, 1.92, 1.08, 2.04, 2.14, 1.76, 1.17) B<-c(1.69, 0.64, .9, 1.41, 1.01, .84, 1.28, 1.59) C<-c(1.56, 1.22, 1.32, 1.39, 1.33, 1.54, 1.04, 2.25, 1.49) D<-c(1.3, .75, 1.26, .69, .62, .9, 1.2, .32) E<-c(.73, .8, .9, 1.24, .82, .72, .57, 1.18, .54, 1.3) ## dis.coal<-makemultdata(A, B, C, D, E, ## cuts = median(c(A, B, C, D, E))) ## em.out<-multmixEM(dis.coal$y, epsilon = 1e-3) ## em.out[1:4]