- formula
formula specifying the response variable and regressors
and partitioning variables, respectively. For details see below.
- data
data.frame to be used for estimating the model tree.
- listw
a weights object for the spatial lag part of the model.
- method
"eigen" (default) - the Jacobian is computed as the product
of (1 - rho*eigenvalue) using eigenw, and "spam" or "Matrix_J" for strictly symmetric weights lists of styles "B" and "C", or made symmetric by similarity (Ord, 1975, Appendix C) if possible for styles "W" and "S", using code from the spam or Matrix packages to calculate the determinant; “Matrix” and “spam_update” provide updating Cholesky decomposition methods; "LU" provides an alternative sparse matrix decomposition approach. In addition, there are "Chebyshev" and Monte Carlo "MC" approximate log-determinant methods; the Smirnov/Anselin (2009) trace approximation is available as "moments". Three methods: "SE_classic", "SE_whichMin", and "SE_interp" are provided experimentally, the first to attempt to emulate the behaviour of Spatial Econometrics toolbox ML fitting functions. All use grids of log determinant values, and the latter two attempt to ameliorate some features of "SE_classic".
- zero.policy
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without
neighbours, if FALSE (default) assign NA - causing lagsarlm() to terminate with an error
- interval
default is NULL, search interval for autoregressive parameter
- control
list of extra control arguments - see lagsarlm
- rhowystart
numeric. A vector of length nrow(data), to be used as
an offset in estimation of the first tree. NULL by default,
which results in an initialization with the root model (without partitioning).
- abstol
numeric. The convergence criterion used for estimation of the model.
When the difference in log-likelihoods of the model from two consecutive
iterations is smaller than abstol, estimation of the model tree has
converged.
- maxit
numeric. The maximum number of iterations to be performed in
estimation of the model tree.
- dfsplit
logical or numeric. as.integer(dfsplit) is the
degrees of freedom per selected split employed when extracting
the log-likelihood.
- verbose
Should the log-likelihood value of the estimated
model be printed for every iteration of the estimation?
- plot
Should the tree be plotted at every iteration of the estimation?
Note that selecting this option slows down execution of the function.
- ...
Additional arguments to be passed to lmtree().
See mob_control documentation for details.