repeatPattern: Create repeated patterns in Bayesian networks

Description

Repeated patterns is a useful model specification short cut for Bayesian networks

Usage

repeatPattern(plist, instances, unlist = TRUE, data = NULL)

Arguments

plist: A list of conditional probability tables. The variable names must have the form name[i] and the i will be substituted by the values given in instances below. See also the data argument.
instances: A vector of distinct integers
unlist: If FALSE the result is a list in which each element is a copy of plist in which name[i] are substituted. If TRUE the result is the result of applying unlist().
data: Enable variable names of the form name[data[i]] - to enable data-driven variable names.

Author

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/.

Examples

Run this code


## Example: Markov chain
yn <- c("yes", "no")

## Specify p(x0)
x.0 <- cptable(~x0, values=c(1, 9), levels=yn)

## Specify transition density
x.x <- cptable(~x[i]|x[i-1], values=c(1, 99, 2, 98), levels=yn)

## Pattern to be repeated
pat <- list(x.x)

rep.pat <- repeatPattern(pat, instances=1:5)
cpt <- compileCPT(c(list(x.0), rep.pat))
mc <- grain(cpt)

if (interactive()) iplot(mc)

## Example: Hidden markov model: The x[i]'s are unobserved, the
## y[i]'s can be observed.

yn <- c("yes", "no")

## Specify p(x0)
x.0 <- cptable(~x0, values=c(1, 9), levels=yn)

## Specify transition density
x.x <- cptable(~x[i]|x[i-1], values=c(1, 99, 2, 98), levels=yn)

## Specify emission density
y.x <- cptable(~y[i]|x[i],   values=c(10, 90, 20, 80), levels=yn)

## The pattern to be repeated
pat <- list(x.x, y.x)

## Repeat pattern and create network
rep.pat <- repeatPattern(pat, instances=1:5)
cpt <- compileCPT(c(list(x.0), rep.pat))
hmm <- grain(cpt)
hmm 

if (interactive()) iplot(hmm)

## Data-driven variable names
x0 <- cptable(~x0, values=c(0.5, 0.5), levels=yn)
x <- cptable(~x[i] | x[data[i, "p"]], values=c(0.5, 0.5), levels=yn)
dep <- data.frame(i=c(1, 2, 3, 4, 5, 6, 7, 8),
                  p=c(0, 1, 2, 2, 3, 3, 4, 4))
x <- repeatPattern(list(x), instances=dep$i, data=dep)
tree <- compileCPT(c(list(x0), x))
tree <- grain(tree)
tree 

if (interactive()) iplot(tree)