stepFlexmix {flexmix} | R Documentation |
Runs flexmix repeatedly for different numbers of components and returns the maximum likelihood solution for each.
stepFlexmix(..., k=NULL, nrep=3, verbose=TRUE, drop=TRUE, unique=FALSE) ## S4 method for signature 'stepFlexmix,missing' plot(x, y, what=c("AIC", "BIC", "ICL"), xlab=NULL, ylab=NULL, legend="topright", ...) ## S4 method for signature 'stepFlexmix' getModel(object, which="BIC") ## S4 method for signature 'stepFlexmix' unique(x, incomparables = FALSE, ...)
... |
|
k |
A vector of integers passed in turn to the |
nrep |
For each value of |
verbose |
If |
drop |
If |
unique |
If |
x, object |
An object of class |
y |
Not used. |
what |
Character vector naming information criteria to
plot. Functions of the same name must exist, which take a
|
xlab,ylab |
Graphical parameters. |
legend |
If not |
which |
Number of model to get. If character, interpreted as number of components or name of an information criterion. |
incomparables |
A vector of values that cannot be
compared. Currently, |
An object of class "stepFlexmix"
containing the best models
with respect to the log likelihood for the different number of
components in a slot if length(k)>1
, else directly an object of
class "flexmix"
.
If unique=FALSE
, then the resulting object contains one
model per element of k
(which is the number of clusters the EM
algorithm started with). If unique=TRUE
, then the result
is resorted according to the number of clusters contained in the
fitted models (which may be less than the number with which the EM
algorithm started), and only the maximum likelihood solution for each
number of fitted clusters is kept. This operation can also be done
manually by calling unique()
on objects of class
"stepFlexmix"
.
Friedrich Leisch and Bettina Gruen
Friedrich Leisch. FlexMix: A general framework for finite mixture models and latent class regression kin R. Journal of Statistical Software, 11(8), 2004. http://www.jstatsoft.org/v11/i08/
data("Nclus") set.seed(511) ## try 5 times for k=4 ex1 <- stepFlexmix(Nclus~1, k=4, model=FLXMCmvnorm(diagonal=FALSE), nrep=5) ex1 ## now 3 times each for k=2:6, specify control parameter ex2 <- stepFlexmix(Nclus~1, k=2:6, model=FLXMCmvnorm(diagonal=FALSE), control=list(minprior=0), nrep=3) ex2 plot(ex2) ## get BIC values BIC(ex2) ## get smallest model getModel(ex2, which=1) ## get model with 3 components getModel(ex2, which="3") ## get model with smallest ICL (here same as for AIC and BIC: true k=4) getModel(ex2, which="ICL") ## now 1 time each for k=2:6, with larger minimum prior ex3 <- stepFlexmix(Nclus~1, k=2:6, model=FLXMCmvnorm(diagonal=FALSE), control=list(minprior=0.1), nrep=1) ex3 ## keep only maximum likelihood solution for each unique number of ## fitted clusters: unique(ex3)