normalmixEM {mixtools} | R Documentation |
Return EM algorithm output for mixtures of normal distributions.
normalmixEM(x, lambda = NULL, mu = NULL, sigma = NULL, k = 2, arbmean = TRUE, arbvar = TRUE, epsilon = 1e-08, maxit = 1000, maxrestarts=20, verb = FALSE, fast=FALSE)
x |
A vector of length n consisting of the data. |
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 mu . |
mu |
A k-vector of initial values for the mean parameters.
If NULL, then the vector is generated from a normal distribution according to a binning method
done on the data. If both lambda and mu are NULL, then number of components is determined by sigma . |
sigma |
A k-vector of initial values for the standard deviation parameters.
If NULL, then 1/sigma $^2$ has random exponential entries according to a binning method done on the data.
If lambda , mu , and sigma are
NULL, then number of components is determined by k . |
k |
Number of components. Ignored unless lambda , mu , and sigma
are all NULL. |
arbmean |
If TRUE, then the component densities are allowed to have different mu s. If FALSE, then
a scale mixture will be fit. |
arbvar |
If TRUE, then the component densities are allowed to have different sigma s. If FALSE, then
a location mixture will be fit. |
epsilon |
The convergence criterion. Convergence is declared when the change in the observed data log-likelihood increases by less than epsilon. |
maxit |
The maximum number of iterations. |
maxrestarts |
The maximum number of restarts allowed in case of a problem with the particular starting values chosen (each restart uses randomly chosen starting values). |
verb |
If TRUE, then various updates are printed during each iteration of the algorithm. |
fast |
If TRUE and k==2 and arbmean==TRUE, then use
normalmixEM2comp , which is a much faster version of the EM
algorithm for this case.
This version is less protected against certain kinds of underflow
that can cause numerical problems and it does not permit any restarts. If
k>2, fast is ignored. |
normalmixEM
returns a list of class mixEM
with items:
x |
The raw data. |
lambda |
The final mixing proportions. |
mu |
The final mean parameters. |
sigma |
The final standard deviations. If arbmean = FALSE, then only the smallest standard
deviation is returned. See scale below. |
scale |
If arbmean = FALSE, then the scale factor for the component standard deviations is returned.
Otherwise, this is omitted from the output. |
loglik |
The final log-likelihood. |
posterior |
An nxk matrix of posterior probabilities for observations. |
all.loglik |
A vector of each iteration's log-likelihood. This vector includes both the initial and the final values; thus, the number of iterations is one less than its length. |
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.
mvnormalmixEM
, normalmixEM2comp
##Analyzing the Old Faithful geyser data with a 2-component mixture of normals. data(faithful) attach(faithful) system.time(out<-normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03)) out system.time(out2<-normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03, fast=TRUE)) out2 # same thing but much faster