components_extractR Documentation

Extract conditional probabilities and clique potentials from data.

Description

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

Usage

extract_cpt(data_, graph, smooth = 0)

extract_pot(data_, graph, smooth = 0)

extract_marg(data_, graph, smooth = 0)

marg2pot(marg_rep)

pot2marg(pot_rep)

extractCPT(data_, graph, smooth = 0)

extractPOT(data_, graph, smooth = 0)

extractMARG(data_, graph, smooth = 0)

Arguments

data_

A named array or a dataframe.

graph

An igraph object or a list or formula which can be turned into a igraph object by calling ug or dag. For extract_cpt, graph must be/define a DAG while for extract_pot, graph must be/define undirected triangulated graph.

smooth

See 'details' below.

marg_rep

An object of class marg_rep

pot_rep

An object of class pot_representation

Details

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

Value

Author(s)

Søren Højsgaard, sorenh@math.aau.dk

References

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

See Also

compileCPT, compilePOT, grain

Examples


## 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, result="igraph")

# UG : [height:species][diam:species]
uG  <- ug(~height:species + diam:species, result="igraph")

pt <- extract_pot(lizard, ~height:species + diam:species) 
cp <- extract_cpt(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)