cptable: Create conditional probability tables (CPTs)
In DataSciBurgoon/gRain: Graphical Independence Networks

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

Creates conditional probability tables of the form p(v|pa(v)).

1	cptable(vpar, levels = NULL, values = NULL, normalize = TRUE, smooth = 0)

`vpar`	Specifications of the names in P(v\|pa1,...pak). See section 'details' for information about the form of the argument.
`levels`	See 'details' below.
`values`	Probabilities; recycled if necessary. Regarding the order, please see section 'details' and the examples.
`normalize`	See 'details' below.
`smooth`	See 'details' below.

If normalize=TRUE then the probabilities are normalized to sum to one for each configuration of the parents.

If smooth is non–zero then zero entries of values are replaced with smooth before normalization takes place.

Regarding the form of the argument vpar: To specify P(a|b,c) one may write ~a|b:c, ~a:b:c, ~a|b+c, ~a+b+c or c("a","b","c"). Internally, the last form is used. Notice that the + and : operator is used as a separator only. The order of the variables IS important so the operators DO NOT commute.

If a has levels a1,a2 and likewise for b and c then the order of values corresponds to the configurations (a1,b1,c1), (a2,b1,c1) (a1,b2,c1), (a2,b2,c1) etc. That is, the first variable varies fastest. Hence the first two elements in values will be the conditional probabilities of a given b=b1, c=c1.

A cptable object (a numeric vector with various attributes).

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/.

andtable, ortable, extractCPT, compileCPT, extractPOT, compilePOT, grain, parray

## See the wet grass example at
## https://en.wikipedia.org/wiki/Bayesian_network

yn <- c("yes", "no")
p.R    <- cptable(~R, values=c(.2, .8), levels=yn)
p.S_R  <- cptable(~S:R, values=c(.01, .99, .4, .6), levels=yn)
p.G_SR <- cptable(~G:S:R, values=c(.99, .01, .8, .2, .9, .1, 0, 1), levels=yn)

# or
ssp <- list(R=yn, S=yn, G=yn) # state space
p.R    <- cptable(~R, values=c(.2, .8), levels=ssp)
p.S_R  <- cptable(~S:R, values=c(.01, .99, .4, .6), levels=ssp)
p.G_SR <- cptable(~G:S:R, values=c(.99, .01, .8, .2, .9, .1, 0, 1), levels=ssp)

# components above are "intermediate representations" and are turned into arrays with
wet.cpt <- compileCPT(p.R, p.S_R, p.G_SR)
wet.cpt
wet.cpt$S # etc

# A Bayesian network is created with:
wet.bn <- grain(wet.cpt)

# Can also create arrays directly
## Not run: 
ssp <- list(R=yn, S=yn, G=yn) # state space
p.R    <- c(.2, .8)
p.S_R  <- c(.01, .99, .4, .6)
p.G_SR <- c(.99, .01, .8, .2, .9, .1, 0, 1)
dim(p.R) <- 2
dimnames(p.R) <- ssp["R"]
dim(p.S_R) <- c(2, 2)
dimnames(p.S_R) <- ssp[c("S", "R")]
dim(p.G_SR) <- c(2, 2, 2)
dimnames(p.G_SR) <- ssp[c("G", "S", "R")]

# Arrays can be created (easier?) with parray() from gRbase
p.R    <- parray("R", levels=ssp, values=c(.2, .8))
p.S_R  <- parray(c("S", "R"), levels = ssp, values=c(.01, .99, .4, .6))
p.G_SR <- parray(~ G:S:R, levels = ssp, values=c(.99, .01, .8, .2, .9, .1, 0, 1))

## End(Not run)