CoCoCg {CoCoCg} | R Documentation |
The CoCoCg package with the shared library of CoCoCg, for data with both discrete and continuous variables, the continuous conditional Gaussian.
Please quit by quitCoCo
to remove temporary files.
Jens Henrik Badsberg
CoCo
.
# Attach data: data(Rats); # Create a CoCoCg-object: CoCoObject <- makeCoCoCg(); # Enter the table of counts into the CoCo-object: enterDataFrame(Rats, object = CoCoObject); # Enter the saturated model into the CoCo-object, and return a model object: fullModel <- makeModel(enterModel("*", object = CoCoObject)); # Display a graph of the model: fullGraph <- dynamic.Graph(fullModel, title = "Full"); # Do a backward elimination of edges: backward(recursive = TRUE, headlong = TRUE, coherent = TRUE, follow = TRUE, object = CoCoObject); # Display the result of the backward elimination: lastModel <- makeModel("last", object = CoCoObject); backwardGraph <- dynamic.Graph(lastModel, title = "Last"); # Do the EH-procedure: eh(object = CoCoObject); # Terminate the CoCo-object with disposing temporary files: endCoCo(object = CoCoObject);