estepE {mclust} | R Documentation |
Implements the expectation step in the EM algorithm for a parameterized MVN mixture model.
estepE(data, mu, sigmasq, pro, eps, warnSingular, Vinv, ...) estepV(data, mu, sigmasq, pro, eps, warnSingular, Vinv, ...) estepEII(data, mu, sigmasq, pro, eps, warnSingular, Vinv, ...) estepVII(data, mu, sigmasq, pro, eps, warnSingular, Vinv, ...) estepEEI(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepVEI(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepEVI(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepVVI(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepEEE(data, mu, Sigma, pro, eps, warnSingular, Vinv, ...) estepEEV(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepVEV(data, mu, decomp, pro, eps, warnSingular, Vinv, ...) estepVVV(data, mu, sigma, pro, eps, 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 unconstrained variance model "VVV" or the equal variance
model "EEE". A d by d by G matrix array whose
[,,k] th entry is the covariance matrix for the kth
component of the mixture model.
|
Sigma |
for the equal variance model "EEE". A d by d matrix giving the common covariance for all components of the mixture model. |
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 .
|
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.
|
... |
Other arguments to describe the variance, in particular
decomp , sigma or cholsigma for model "VVV",
decomp for models "VII" and "EII", and Sigma or
cholSigma for model "EEE". Sigma is an d by
d matrix giving the common covariance for all components of
the mixture model.
Also used to catch unused arguments from a do.call call.
|
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. |
modelName |
Character string identifying the model. |
Attribute |
|
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association. 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.
estep
,
em
,
mstep
,
do.call
,
mclustOptions
data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5] msEst <- mstepEII(data = irisMatrix, z = unmap(irisClass)) names(msEst) estepEII(data = irisMatrix, mu = msEst$mu, pro = msEst$pro, sigmasq = msEst$sigmasq) ## Not run: do.call("estepEII", c(list(data=irisMatrix), msEst)) ## alternative call ## End(Not run)