SpatialProbit package allows to fit spatial autoregressive (SAR) and
spatial error (SEM) probit models. It also provides functions to simulated
spatial binary data, an empirical data set and different methods for the
diagnostic of the estimated model.
SpatialProbitFit. It allows to
fit both SAR and SEM models for big datasets in a reasonable time. The
function is based on the maximisation of the approximate likelihood
function. The approximation is inspired by the Mendell and Elston algorithm
for computing multivariate normal probabilities and take advantage of the
sparsity of the spatial weight matrix. Two methods are available for the
estimation of the model parameter: the first one is known as conditional
method (see Case (1992)) and performs relatively well in terms of accuracy
of the estimated parameters and is very rapid. The second method, that
minimises the full-log-likelihood, is slower but it should be more accurate.
Monte Carlo experiments on simulated data reported in Martinetti and Geniaux
(2015) showed that the full-log-likelihood approach is not always
overperforming the conditional method in terms of accuracy. At the present
stage, our suggestion is to use the conditional method for a first
estimation and only attempt the full-likelihood approach in a second moment,
when the dataset size is not bigger than a few thousands.Another feature of the SpatialProbitFit function is the possibility
to fit the model using the precision matrix instead of the
variance-covariance matrix, since it is usually sparser and hence allows
faster computations (see LeSage and Pace (2009)).
The output of SpatialProbitFit function is an object of class
SpatialProbit, for which the methods
residuals, fitted, effects, predict and
coef are available.
The package also contains the function sim_binomial_probit that
allows to simulate data samples of both SAR and SEM models. It can be used
to replicate the Monte Carlo experiments reported in Martinetti and Geniaux
(2015) as well as the experiment of Calabrese and Elkink (2014).
An empirical data set Katrina on the reopening decisions of
firms in the aftermath of the Katrina Hurricane in New Orleans is also
available (LeSage et al.(2011)).
Other packages in CRAN repository on the same subject are
McSpatial (McMillen (2013)) and
spatialprobit (Wilhelm and Godinho de Matos
(2013)).
The core functions of the present package have been coded using the
Rcpp and RcppEigen libraries (Bates and Eddelbuettel (2013)),
that allow direct interchange of rich R objects between R and C++.