Package edu.cmu.tetrad.search.work_in_progress

Class Washdown

java.lang.Object
edu.cmu.tetrad.search.work_in_progress.Washdown
public class Washdown extends Object
Implements the Washdown algorithm,

Initialization: Current Model = M1 := single factor pure model in which L1 is a cause of all Vi in v

1) Purify step: run Purify on getModel model. 2) Create new model: for each indicator Vi in Vdiscard (variables discarded) by Purify in step 1, move Vi from being an effect of its latent Lj to being an effect of Lj+1, where if Lj+1 does not exist, create it and freely correlate Lj+1 with all latents L1 to Lj. 3) stop check: estimate new model and stop if goodness-of-fit test passes, else getModel model:= new model, and go to step 1.

Purify 1) Vkeep := v, Vdiscard := null set 2) Do goodness-of-fit test on getModel model Mc, stop if Mc passes, return Vkeep and Vdiscard 2) For each indicator Vi, do goodness-of-fit test on Mc - Vi, store foodness-of-fit test score as gof(Vi) 3) New getModel model := Mc - Vi, for Vi with max gof(Vi) from step 3. 4) Vkeep:= Vkeep - Vi, Vdiscard:= Vdiscard + Vi 5) Go to step 2.

Clearly we can use any goodness of fit test we think is appropriate - the default being the chi-square test.

Version:
$Id: $Id
Author:
josephramsey