repeatPattern: Create repeated patterns in Bayesian networks

In DataSciBurgoon/gRain: Graphical Independence Networks

Description

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

Usage

repeatPattern(plist, instances, unlist = TRUE)

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.
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().

Author(s)

Søren Højsgaard, [e-mail]

References

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/.

See Also

grain, compileCPT

Examples

## 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)

DataSciBurgoon/gRain documentation built on March 25, 2020, 12:02 a.m.