Identifiers are functions that take as input a covariance matrix Sigma
corresponding to some mixed graph G and, from that covariance matrix,
identify some subset of the coefficients in the mixed graph G. This function
takes as input the matrices, L and O, defining G and creates an identifier
that does not identify any of the coefficients of G. This is useful as a
base case when building more complex identification functions.
createIdentifierBaseCase(L, O)a function that takes as input a covariance matrix compatible with
the mixed graph defined by L/O and returns a list with two
named components:
Lambdaa matrix equal to L but with NA values instead of 1s
Omegaa matrix equal to O but with NA values instead of 1s
When building more complex identifiers these NAs will be replaced by the value that can be identified from Sigma.
Adjacency matrix for the directed part of the path diagram/mixed graph; an edge pointing from i to j is encoded as L[i,j]=1 and the lack of an edge between i and j is encoded as L[i,j]=0. There should be no directed self loops, i.e. no i such that L[i,i]=1.
Adjacency matrix for the bidirected part of the path diagram/mixed graph. Edges are encoded as for the L parameter. Again there should be no self loops. Also this matrix will be coerced to be symmetric so it is only necessary to specify an edge once, i.e. if O[i,j]=1 you may, but are not required to, also have O[j,i]=1.