emE {mclust} | R Documentation |
Implements the EM algorithm for a parameterized MVN mixture model, starting with the expectation step.
emE(data, mu, sigmasq, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emV(data, mu, sigmasq, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emEII(data, mu, sigmasq, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emVII(data, mu, sigmasq, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emEEI(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emVEI(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emEVI(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emVVI(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emEEE(data, mu, Sigma, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emEEV(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emVEV(data, mu, decomp, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...) emVVV(data, mu, sigma, pro, eps, tol, itmax, equalPro, warnSingular, Vinv, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
mu |
The mean for each component. If there is more than one component,
mu is a matrix whose columns are the means of the
components.
|
sigmasq |
for the one-dimensional models ("E", "V") and spherical models ("EII", "VII"). This is either a vector whose kth component is the variance for the kth component in the mixture model ("V" and "VII"), or a scalar giving the common variance for all components in the mixture model ("E" and "EII"). |
decomp |
for the diagonal models ("EEI", "VEI", "EVI", "VVI") and some
ellipsoidal models ("EEV", "VEV"). This is a list described in more
detail in cdens .
|
Sigma |
for the equal variance model "EEE". A d by d matrix giving the common covariance for all components of the mixture model. |
sigma |
for the unconstrained variance model "VVV".
A d by d by G matrix array whose
[,,k] th entry is the covariance matrix for the
kth component of the mixture model.
|
... |
An argument giving the variance that takes one of the following forms:
The form of the variance specification is the same as for the output for the em , me , or mstep methods for the
specified mixture model.
Also used to catch unused arguments from a do.call call.
|
pro |
Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of MVN components if the mixture model includes a Poisson noise term. |
eps |
A scalar tolerance for deciding when to terminate computations due to
computational singularity in covariances.
Smaller values of eps allow computations to
proceed nearer to singularity.
The default is .Mclust\$eps .
|
tol |
A scalar tolerance for relative convergence of the loglikelihood values.
The default is .Mclust\$tol .
|
itmax |
An integer limit on the number of EM iterations.
The default is .Mclust\$itmax .
|
equalPro |
A logical value indicating whether or not the components in the model are
present in equal proportions.
The default is .Mclust\$equalPro .
|
warnSingular |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is .Mclust\$warnSingular .
|
Vinv |
An estimate of the reciprocal hypervolume of the data region.
The default is determined by applying function
hypvol to the data.
Used only when pro includes an additional
mixing proportion for a noise component.
|
This function can be used with an indirect or list call
using do.call
, allowing the output
of e.g. mstep
to be passed
without the need to specify individual parameters as arguments.
A list including the following components:
z |
A matrix whose [i,k] th entry is the
conditional probability of the ith observation belonging to
the kth component of the mixture.
|
loglik |
The logliklihood for the data in the mixture model. |
mu |
A matrix whose kth column is the mean of the kth component of the mixture model. |
sigma |
For multidimensional models, a three dimensional array
in which the [,,k] th entry gives the
the covariance for the kth group in the best model. <br>
For one-dimensional models, either a scalar giving a common variance for
the groups or a vector whose entries are the variances for each group
in the best model.
|
pro |
A vector whose kth component is the mixing proportion for the kth component of the mixture model. |
modelName |
Character string identifying the model. |
Attributes: |
|
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
em
,
mstep
,
mclustOptions
,
do.call
data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5] msEst <- mstepEEE(data = irisMatrix, z = unmap(irisClass)) names(msEst) emEEE(data = irisMatrix, mu = msEst$mu, pro = msEst$pro, cholSigma = msEst$cholSigma) ## Not run: do.call("emEEE", c(list(data=irisMatrix), msEst)) ## alternative call ## End(Not run)