"HMgmm"
class is the class of homogeneous mixed graphical
Markov models defined within the qpgraph
package to store
simulate and manipulate this type of graphical Markov models (GMMs).An homogeneous mixed GMM is a family of multivariate conditional Gaussian distributions on mixed discrete and continuous variables sharing a set of conditional independences encoded by means of a marked graph. Further details can be found in the book of Lauritzen (1996).
HMgmm(g, ...)
corresponding
to constructor methods or rHMgmm(n, g, ...)
corresponding to random
simulation methods.pI
:"integer"
storing the number of
discrete random variables.pY
:"integer"
storing the number of
continuous random variables.g
:graphBAM-class
storing
the associated marked graph.vtype
:"factor"
storing the type (discrete
or continuous) of each random variable.dLevels
:"integer"
storing the number of
levels of each discrete random variable.a
:"numeric"
storing the vector of additive
linear effects on continuous variables connected to discrete ones.rho
:"numeric"
storing the value of the
marginal correlation between two continuous random variables.sigma
:dspMatrix-class
storing the covariance matrix.mean
:"numeric"
storing the mean vector.eta2
:"numeric"
storing for each continuous
variable connected to a discrete one, the fraction of variance of the
continuous variable explained by the discrete one.HMgmm(g)
g
can be either an
adjacency matrix or a graphBAM-class
object.rHMgmm(n, g)
n
is the number of GMMs to
simulate and g
can be either a markedGraphParam object,
an adjacency matrix or a graphBAM-class
object.names(x)
x
that can be retrieved with the $
accessor operator.$
list
.dim(x)
dimnames(x)
show(object)
object
.summary(object)
object
.plot(x, ...)
x
. It uses the plotting
capabilities from the Rgraphviz
library to which further arguments
specified in ...
are further passed.