flexmix {flexmix} | R Documentation |
FlexMix implements a general framework for finite
mixtures of regression models. Parameter estimation is performed using
the EM algorithm: the E-step is implemented by flexmix
, while
the user can specify the M-step.
flexmix(formula, data = list(), k = NULL, cluster = NULL, model=NULL, concomitant=NULL, control = NULL, weights = NULL) ## S4 method for signature 'flexmix' summary(object, eps=1e-4, ...)
formula |
A symbolic description of the model to be fit. The
general form is |
data |
An optional data frame containing the variables in the model. |
k |
Number of clusters (not needed if |
cluster |
Either a matrix with |
weights |
An optional vector of weights to be used in the fitting
process. Should be |
model |
Object of class |
concomitant |
Object of class |
control |
Object of class |
object |
Object of class |
eps |
Probabilities below this threshold are treated as zero in the summary method. |
... |
Currently not used. |
FlexMix models are described by objects of class FLXM
,
which in turn are created by driver functions like
FLXMRglm
or FLXMCmvnorm
. Multivariate
responses with independent components can be specified using a
list of FLXM
objects.
The summary
method lists for each component the prior
probability, the number of observations assigned to the corresponding
cluster, the number of observations with a posterior probability
larger than eps
and the ratio of the latter two numbers (which
indicates how separated the cluster is from the others).
Returns an object of class flexmix
.
Friedrich Leisch and Bettina Gruen
Friedrich Leisch. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11(8), 2004. http://www.jstatsoft.org/v11/i08/
data("NPreg") ## mixture of two linear regression models. Note that control parameters ## can be specified as named list and abbreviated if unique. ex1 <- flexmix(yn~x+I(x^2), data=NPreg, k=2, control=list(verb=5, iter=100)) ex1 summary(ex1) plot(ex1) ## now we fit a model with one Gaussian response and one Poisson ## response. Note that the formulas inside the call to FLXMRglm are ## relative to the overall model formula. ex2 <- flexmix(yn~x, data=NPreg, k=2, model=list(FLXMRglm(yn~.+I(x^2)), FLXMRglm(yp~., family="poisson"))) plot(ex2) ex2 table(ex2@cluster, NPreg$class) ## for Gaussian responses we get coefficients and standard deviation parameters(ex2, component=1, model=1) ## for Poisson response we get only coefficients parameters(ex2, component=1, model=2) ## fitting a model only to the Poisson response is of course ## done like this ex3 <- flexmix(yp~x, data=NPreg, k=2, model=FLXMRglm(family="poisson")) ## if observations are grouped, i.e., we have several observations per ## individual, fitting is usually much faster: ex4 <- flexmix(yp~x|id1, data=NPreg, k=2, model=FLXMRglm(family="poisson")) ## And now a binomial example. Mixtures of binomials are not generically ## identified, here the grouping variable is necessary: set.seed(1234) ex5 <- stepFlexmix(cbind(yb,1-yb)~x, data=NPreg, k=2, model=FLXMRglm(family="binomial"), nrep=5) table(NPreg$class, clusters(ex5)) ex6 <- stepFlexmix(cbind(yb,1-yb)~x|id2, data=NPreg, k=2, model=FLXMRglm(family="binomial"), nrep=5) table(NPreg$class, clusters(ex6))