R/SEMID.R

In Lucaweihs/SEMID: Identifiability of Linear Structural Equation Models

#' SEMID package documentation.
#'
#' SEMID provides a number of methods for testing the global/generic
#' identifiability of mixed graphs and latent-factor graphs.
#'
#' The only functions you're likely to need from \pkg{SEMID} are
#' \code{\link{semID}} and \code{\link{lfhtcID}}. A complete description of all package features, along
#' with examples, can be found at \url{https://github.com/Lucaweihs/SEMID}.
#'
#' @import igraph
#' @import utils
#' @importFrom graphics plot
#' @importFrom R.oo setConstructorS3
#' @importFrom R.methodsS3 setMethodS3
#'
#' @name SEMID-package
#' @aliases SEMID
#'
#' @examples
#' ###
#' # Checking the generic identifiability of parameters in a mixed graph.
#' ###
#'
#' # Mixed graphs are specified by their directed adjacency matrix L and
#' # bidirected adjacency matrix O.
#' L = t(matrix(
#'  c(0, 1, 1, 0, 0,
#'    0, 0, 1, 1, 1,
#'    0, 0, 0, 1, 0,
#'    0, 0, 0, 0, 1,
#'    0, 0, 0, 0, 0), 5, 5))
#'
#' O = t(matrix(
#'  c(0, 0, 0, 1, 0,
#'    0, 0, 1, 0, 1,
#'    0, 0, 0, 0, 0,
#'    0, 0, 0, 0, 0,
#'    0, 0, 0, 0, 0), 5, 5)); O=O+t(O)
#'
#' # Create a mixed graph object
#' graph = MixedGraph(L, O)
#'
#' # We can plot what this mixed graph looks like, blue edges are directed
#' # red edges are bidirected.
#' plot(graph)
#'
#' # Without using decomposition techniques we can't identify all nodes
#' # just using the half-trek criterion
#' htcID(graph, tianDecompose = FALSE)
#'
#' # The edgewiseTSID function can show that all edges are generically
#' # identifiable without proprocessing with decomposition techniques
#' edgewiseTSID(graph, tianDecompose = FALSE)
#'
#' # The above shows that all edges in the graph are generically identifiable.
#' # See the help of edgewiseTSID to find out more information about what
#' # else is returned by edgewiseTSID.
#'
#' ###
#' # Checking generic parameter identifiability using the generalGenericID
#' # function
#' ###
#'
#' L = t(matrix(
#'  c(0, 1, 0, 0, 0,
#'    0, 0, 0, 1, 1,
#'    0, 0, 0, 1, 0,
#'    0, 1, 0, 0, 1,
#'    0, 0, 0, 1, 0), 5, 5))
#'
#' O = t(matrix(
#'  c(0, 0, 0, 0, 0,
#'    0, 0, 1, 0, 1,
#'    0, 0, 0, 1, 0,
#'    0, 0, 0, 0, 0,
#'    0, 0, 0, 0, 0), 5, 5)); O=O+t(O)
#'
#' # Create a mixed graph object
#' graph = MixedGraph(L, O)
#'
#' # Now lets define an "identification step" function corresponding to
#' # using the edgewise identification algorithm but with subsets
#' # controlled by 1.
#' restrictedEdgewiseIdentifyStep <- function(mixedGraph,
#'                                             unsolvedParents,
#'                                             solvedParents,
#'                                             identifier) {
#'      return(edgewiseIdentifyStep(mixedGraph, unsolvedParents,
#'                                  solvedParents, identifier,
#'                                  subsetSizeControl = 1))
#' }
#'
#' # Now we run an identification algorithm that iterates between the
#' # htc and the "restricted" edgewise identification algorithm
#' generalGenericID(graph, list(htcIdentifyStep,
#'                                restrictedEdgewiseIdentifyStep),
#' 	                 tianDecompose = FALSE)
#'
#' # We can do better (fewer unsolved parents) if we don't restrict the edgewise
#' # identifier algorithm as much
#' generalGenericID(graph, list(htcIdentifyStep, edgewiseIdentifyStep),
#'                   tianDecompose = FALSE)
#'
#' ###
#' # Checking the generic identifiability of parameters in a latent-factor graph.
#' ###
#'
#' # Latent digraphs are specified by their directed adjacency matrix L
#' library(SEMID)
#' L = matrix(c(0, 1, 0, 0, 0, 0,
#'              0, 0, 1, 0, 0, 0,
#'              0, 0, 0, 0, 0, 0,
#'              0, 0, 0, 0, 1, 0,
#'              0, 0, 0, 0, 0, 0,
#'              1, 1, 1, 1, 1, 0), 6, 6, byrow=TRUE)
#' observedNodes = seq(1,5)
#' latentNodes = c(6)
#'
#' # Create the latent digraph object corresponding to L
#' g = LatentDigraph(L, observedNodes, latentNodes)
#'
#' # Plot latent digraph
#' plot(g)
#'
#' # We can identify all nodes by the latent-factor half-trek criterion
#' lfhtcID(g)
#'
NULL