repeat_pattern.RdRepeated patterns is a useful model specification short cut for Bayesian networks
repeat_pattern(plist, instances, unlist = TRUE, data = NULL)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.
A vector of consecutive integers
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().
A two column matrix. The first column is the index / name of a node; the second column is the index / name of the node's parent.
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/.
yn <- c("yes", "no")
n <- 3
## Example: Markov chain
x_init <- cpt(~x0, values=c(1, 9), levels=yn) ## p(x0)
x_trans <- cpt(~x[i]|x[i-1], values=c(1, 99, 2, 98), levels=yn) ## p(x[i]|x[i-1])
pat <- list(x_trans)
rep.pat <- repeat_pattern(pat, instances=1:n)
mc <- compile_cpt(c(list(x_init), rep.pat))
mc
#> P( x0 )
#> P( x1 | x0 )
#> P( x2 | x1 )
#> P( x3 | x2 )
mc <- mc |> grain()
## Example: Hidden markov model:
# The x[i]'s are unobserved, the y[i]'s can be observed.
x_init <- cpt(~x0, values=c(1, 9), levels=yn) ## p(x0)
x_trans <- cpt(~x[i]|x[i-1], values=c(1, 99, 2, 98), levels=yn) ## p(x[i]|x[i-1])
y_emis <- cpt(~y[i]|x[i], values=c(10, 90, 20, 80), levels=yn) ## p(y[i]|x[i])
pat <- list(x_trans, y_emis) ## Pattern to be repeated
rep.pat <- repeat_pattern(pat, instances=1:n)
hmm <- compile_cpt(c(list(x_init), rep.pat))
hmm
#> P( x0 )
#> P( x1 | x0 )
#> P( y1 | x1 )
#> P( x2 | x1 )
#> P( y2 | x2 )
#> P( x3 | x2 )
#> P( y3 | x3 )
hmm <- hmm |> grain()
## Data-driven variable names
dep <- data.frame(i=c(1, 2, 3, 4, 5, 6, 7, 8),
p=c(0, 1, 2, 2, 3, 3, 4, 4))
x0 <- cpt(~x0, values=c(0.5, 0.5), levels=yn)
xa <- cpt(~x[i] | x[data[i, "p"]], values=c(1, 9, 2, 8), levels=yn)
xb <- repeat_pattern(list(xa), instances=1:nrow(dep), data=dep)
tree <- compile_cpt(c(list(x0), xb))
tree
#> P( x0 )
#> P( x1 | x0 )
#> P( x2 | x1 )
#> P( x3 | x2 )
#> P( x4 | x2 )
#> P( x5 | x3 )
#> P( x6 | x3 )
#> P( x7 | x4 )
#> P( x8 | x4 )
tree <- tree |> grain()
tree
#> Independence network: Compiled: TRUE Propagated: FALSE Evidence: FALSE