computeDiagonalization

Estimate for connectivity matrix returned by <code>backShift</code>.

estConnectivity

Indicator of the experiment or the intervention type an 
observation belongs to (a numeric vector of length n).

Indicator for the environment for which the matrix $\Delta\Sigma_{c,j}$$ should be computed.

whichEnv

Optional title for plot; defaults to paste("Env.", whichEnv)

main

Computes the matrix $\Delta \Sigma_{c,j}$ resulting from the joint diagonalization 
for a given environment (cf. Eq.(7) in the paper). 
If the joint diagonalization was successful the matrix should 
be diagonal for all environments $j$.

Code for 'backShift', an algorithm to estimate the connectivity
matrix of a directed (possibly cyclic) graph with hidden variables. The
underlying system is required to be linear and we assume that observations
under different shift interventions are available. For more details,
see <arXiv:1506.02494>.

Christina Heinze-Deml

backShift

Learning Causal Cyclic Graphs from Unknown Shift Interventions

computeDiagonalization function

Computes the matrix $\Delta \Sigma_{c,j}$ resulting from the joint diagonalization 
for a given environment (cf. Eq.(7) in the paper). 
If the joint diagonalization was successful the matrix should 
be diagonal for all environments $j$. — computeDiagonalization

Computes the matrix $\Delta \Sigma_{c,j}$ resulting from the joint diagonalization 
for a given environment (cf. Eq.(7) in the paper). 
If the joint diagonalization was successful the matrix should 
be diagonal for all environments $j$.

computeDiagonalization: Computes the matrix \(\Delta \Sigma_{c,j}\) resulting from the joint diagonalization for a given environment (cf. Eq.(7) in the paper). If the joint diagonalization was successful the matrix should be diagonal for all environments $j$.

Description

Usage

Arguments