components_extract: Extract conditional probabilities and clique potentials from...
In DataSciBurgoon/gRain: Graphical Independence Networks

Description Usage Arguments Details Value Author(s) References See Also Examples

Extract list of conditional probability tables and list of clique potentials from data.

extractCPT(data_, graph, smooth = 0)

extractPOT(data_, graph, smooth = 0)

extractMARG(data_, graph, smooth = 0)

marg2pot(mg)

pot2marg(pt)

`data_`	A named array or a dataframe.
`graph`	A `graphNEL` object or a list or formula which can be turned into a `graphNEL` object by calling `ug` or `dag`. For `extractCPT`, graph must be/define a DAG while for `extractPOT`, graph must be/define undirected triangulated graph.
`smooth`	See 'details' below.
`mg`	An object of class `marg_rep`
`pt`	An object of class `pot_rep`

If smooth is non-zero then smooth is added to all cell counts before normalization takes place.

extractCPT: A list of conditional probability tables.
extractPOT: A list of clique potentials.
extractMARG: A list of clique marginals.

Søren Højsgaard, sorenh@math.aau.dk

Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. http://www.jstatsoft.org/v46/i10/.

compileCPT, compilePOT, grain

## Extract cpts / clique potentials from data and graph
# specification and create network. There are different ways:

data(lizard, package="gRbase")

# DAG: height <- species -> diam
daG <- dag(~species + height:species + diam:species)

# UG : [height:species][diam:species]
uG  <- ug(~height:species + diam:species)

pt <- extractPOT(lizard, ~height:species + diam:species) 
cp <- extractCPT(lizard, ~species + height:species + diam:species)

pt
cp

# Both specify the same probability distribution
tabListMult(pt) %>% as.data.frame.table
tabListMult(cp) %>% as.data.frame.table

## Not run: 
# Bayesian networks can be created as
bn.uG   <- grain(pt)
bn.daG  <- grain(cp)

# The steps above are wrapped into a convenience method which
# builds a network from at graph and data.
bn.uG   <- grain(uG, data=lizard)
bn.daG  <- grain(daG, data=lizard)

## End(Not run)