is it posible to fit a multivariate linear model using lmtree?

I want to fit a lmtree where the response is multivariable. You know that you can fit a Multivariate Multiple Regression using lm function as follows:

lm(cbind(Y1, Y2) ~ X1+X2+X3, data = my_df)

so I tried to do this in the context of lmtree:

lmtree(cbind(Y1,Y2) ~ Z1 | X1+X2+X3,data = my_df)

but it seems it is not implemented, I got this error message:

Error in x[, !is.na(z$coefficients), drop = FALSE] : 
  (subscript) logical subscript too long

It is possible to fit a lmtree with a multivariable response? if so, what is the correct way to specify it?

Currently, lmtree() is only geared towards univariate response variables and multivariate responses are not supported.

In principle, the MOB approach can also be used for multivariate linear models, though. Using the partykit:::lmfit() function as a starting point would also work but:

• the matrix of coefficients from the mlm needs to be turned into a suitably named vector
• the corresponding estfun computation needs to be adjusted correspondingly (see sandwich:::estfun.mlm)
• the objfun computation should be checked but I guess this works ok
• when calling mob() the control option ytype = "matrix" needs to be specified

If there are no other problems which require adaptation of the code, then it might also be possible to wire this into lmtree() but I haven't got the time to check this now.