Marginals | R Documentation |
Get the marginal distributions of multiple variables
Marginals(tree, vars)
tree |
a |
vars |
a |
Get the marginal distributions of multiple variables. The function Marginals
returns a list
of marginal distributions. The marginal distribution of a discrete variable
is a named vector of probabilities. Meanwhile, the marginal distributions of
continous variables in a CG-BN model are mixtures of Gaussian distributions.
To fully represent this information, the marginal of a continuous variable is represented by
a data.frame
with three columns to specify
parameters for each Gaussian distribution in the mixture, which are
mean
the mean value of a Gaussian distribution.
sd
the standard deviation of a Gaussian distribution.
n
the number of Gaussian mixtures
marginals
a list
of marginal distributions
types
a named vector
indicating the types of the variables whose
marginals are queried: TRUE
for discrete, FALSE
for continuous.
Han Yu
Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks.
Journal of Machine Learning Research, 6(Sep), 1517-1550.
Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian
Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.
PlotMarginals
for visualization of the marginal distributions,
SummaryMarginals
for summarization of the marginal distributions of
continuous variables.
data(liver) tree.init.p <- Initializer(dag=liver$dag, data=liver$data, node.class=liver$node.class, propagate = TRUE) tree.post <- AbsorbEvidence(tree.init.p, c("Nr1i3", "chr1_42.65"), list(1,"1")) marg <- Marginals(tree.post, c("HDL", "Ppap2a")) marg$marginals$HDL head(marg$marginals$Ppap2a)
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