
flexmix
, while
the user can specify the M-step.flexmix(formula, data = list(), k = NULL, cluster = NULL,
model=NULL, control = NULL)
## S3 method for class 'flexmix':
summary(object, eps=1e-4, ...)
y~x|g
where y
is the response,
x
the set of predictors and g
an optional grouping
factor for repeated measurements.cluster
is specified).k
clusters).FLXmodel
of list of FLXmodel
objects. Default is the object returned by calling
FLXglm()
.FLXcontrol
or a named list.flexmix
.flexmix
.FLXmodel
,
which in turn are created by driver functions like
FLXglm
or FLXmclust
. Multivariate
responses with independent components can be specified using a
list of FLXmodel
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).
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 FLXglm are
## relative to the overall model formula.
ex2 <- flexmix(yn~x, data=NPreg, k=2,
model=list(FLXglm(yn~.+I(x^2)),
FLXglm(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=FLXglm(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=FLXglm(family="poisson"))
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