The ess package is an R implementation of the algorithm presented in this paper and later corrected slightly in this paper. The ESS algorithm is used for model selection in discrete decomposable graphical models. It is fast compared to other model selection procedures in R, especially when data is high-dimensional.
The class of graphical models is a family of probability distributions for which conditional dependencies can be read off from a graph. If the graph is decomposable, the maximum likelihood estimates of the parameters in the model can be shown to be on exact form. This is what enables ESS to be fast and efficient.
You can install the current stable release of the package by using
the devtools package:
devtools::install_github("mlindsk/ess", build_vignettes = FALSE)The main function in ess is fit_graph
which fits a decomposable graph. An object returned from
fit_graph is a gengraph object.
fit_graph has four types; forward selection
(fwd), backward selection (bwd), tree
(tree) and a combination of tree and forward
(tfwd). Using adj_lst on an object returned by
fit_graph gives the adjacency list corresponding to the
graph. Similarly one can use adj_mat to obtain an adjacency
matrix.
A neat usecase of ess is that of variable selection.
Consider the built-in data derma (dermatitis) with class
variable ES. We can fit a graph structure to this data, and
inspect the graph to see which variables ES directly
depends upon:
library(ess)
g <- fit_graph(derma)
plot(g, vertex.size = 1)
Instead of inspecting the graph (it can be difficult if there are
many variables) we can simply extract the neighbors of
ES
adj <- adj_lst(g)
adj$ES
#> [1] "h21" "h20" "h28" "h33" "h16" "h29" "c9" "h15" "h14" "c5" "c3" "h19"
#> [13] "h26" "c4" "age" "c7" "c2" "h31" "c1" "h18" "h17" "h32" "c11" "h13"
#> [25] "h23" "c10" "h22" "h24" "h30" "h27"For more information, see the documentation. E.g. type
?fit_graph in an R session.
The molic package is
used for outlier detection in categorical data and is designed to work
with gengraph objects. One can use ess to
fit a gengraph object, extract the adjacency matrix, conert
it to an igraph object and use it in connection with belief
propagation via the jti
package.