Extract conditional probabilities and clique potentials from data.

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

extract_cpt(data_, graph, smooth = 0)

extract_pot(data_, graph, smooth = 0)

extract_marg(data_, graph, smooth = 0)

marg2pot(marg_rep)

pot2marg(pot_rep)

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

Value

• extract_cpt: A list of conditional probability tables.

• extract_pot: A list of clique potentials.

• extract_marg: A list of clique marginals.

Details

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

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

Author

Søren Højsgaard, sorenh@math.aau.dk

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
#> [[1]]
#>         species
#> height       anoli      dist
#>   >4.75  0.1051345 0.2493888
#>   <=4.75 0.2958435 0.3496333
#> 
#> [[2]]
#>      species
#> diam      anoli      dist
#>   <=4 0.7195122 0.5469388
#>   >4  0.2804878 0.4530612
#> 
#> attr(,"rip")
#> cliques
#>   1 : height species 
#>   2 : species diam 
#> separators
#>   1 :  
#>   2 : species 
#> parents
#>   1 : 0 
#>   2 : 1 
#> attr(,"graph")
#> IGRAPH 09047b4 UN-- 3 2 -- 
#> + attr: name (v/c)
#> + edges from 09047b4 (vertex names):
#> [1] height --species species--diam   
#> attr(,"class")
#> [1] "pot_representation"
cp
#> [[1]]
#> species
#>    anoli     dist 
#> 0.400978 0.599022 
#> 
#> [[2]]
#>         species
#> height       anoli      dist
#>   >4.75  0.2621951 0.4163265
#>   <=4.75 0.7378049 0.5836735
#> 
#> [[3]]
#>      species
#> diam      anoli      dist
#>   <=4 0.7195122 0.5469388
#>   >4  0.2804878 0.4530612
#> 
#> attr(,"graph")
#> IGRAPH 81b8d28 DN-- 3 2 -- 
#> + attr: name (v/c)
#> + edges from 81b8d28 (vertex names):
#> [1] species->height species->diam  
#> attr(,"class")
#> [1] "cpt_representation"

# Both specify the same probability distribution
tabListMult(pt) |> as.data.frame.table()
#>   height diam species       Freq
#> 1  >4.75  <=4   anoli 0.07564554
#> 2 <=4.75  <=4   anoli 0.21286302
#> 3  >4.75   >4   anoli 0.02948894
#> 4 <=4.75   >4   anoli 0.08298050
#> 5  >4.75  <=4    dist 0.13640038
#> 6 <=4.75  <=4    dist 0.19122798
#> 7  >4.75   >4    dist 0.11298837
#> 8 <=4.75   >4    dist 0.15840527
tabListMult(cp) |> as.data.frame.table()
#>   height diam species       Freq
#> 1  >4.75  <=4   anoli 0.07564554
#> 2 <=4.75  <=4   anoli 0.21286302
#> 3  >4.75   >4   anoli 0.02948894
#> 4 <=4.75   >4   anoli 0.08298050
#> 5  >4.75  <=4    dist 0.13640038
#> 6 <=4.75  <=4    dist 0.19122798
#> 7  >4.75   >4    dist 0.11298837
#> 8 <=4.75   >4    dist 0.15840527

if (FALSE) { # \dontrun{
# 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)
} # }