Shortcut for homogenous networks give weird result on diagonal

[JoFAM]

I tried the following in test_shortcuts.R

  looE <- loo(modh)
  looEtest <- sapply(seq_len(ncol(Yh)),
                     function(y) sapply(seq_len(nrow(Yh)),
                                        function(x){Ytilde <- Yh
                                        Ytilde[x,y] <- looE[x,y]
                                        Ytilde[y,x] <- looE[x,y]
                                        predict_ij(Ytilde, Hkh, Hkh, x, y) }
                     )
  )

If I do so, all loo predictions are correctly calculated, except for the values on the diagonal. This is currently solved by simply ignoring the diagonal :

  looE <- loo(modh)
  looEtest <- sapply(seq_len(ncol(Yh)),
                     function(y) sapply(seq_len(nrow(Yh)),
                                        function(x){Ytilde <- Yh
                                        Ytilde[x,y] <- looE[x,y]
                                        Ytilde[y,x] <- looE[x,y]
                                        if(x == y) Ytilde[x,y] else
                                        predict_ij(Ytilde, Hkh, Hkh, x, y) }
                     )
  )

Is this enough or is there something going wrong in that procedure?

[JoFAM]

@MichielStock any ideas on what's going on here?

[JoFAM]

@MichielStock Here's a piece of code based on your own unit testing that shows how it goes wrong on the diagonal.
LOO_homogenous_problem.zip

[JoFAM]

Can't find a solution. As a diagonal in a symmetric network is a special case (interaction with oneself is usually a fixed value), this is ignored for now.