GMerrorsar               package:spdep               R Documentation

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_D_e_s_c_r_i_p_t_i_o_n:

     An implementation of Kelejian and Prucha's generalised moments
     estimator for the autoregressive parameter in a spatial model.

_U_s_a_g_e:

     GMerrorsar(formula, data = list(), listw, na.action = na.fail,
      zero.policy = FALSE, return_LL = FALSE, method="nlminb", 
      control = list(), pars, verbose=FALSE, sparse_method="Matrix",
      returnHcov=FALSE, pWOrder=250, tol.Hcov=1.0e-10)
     ## S3 method for class 'gmsar':
     summary(object, correlation = FALSE, Hausman=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a symbolic description of the model to be fit. The details 
          of model specification are given for 'lm()'

    data: an optional data frame containing the variables in the model.
           By default the variables are taken from the environment
          which the function  is called.

   listw: a 'listw' object created for example by 'nb2listw'

na.action: a function (default 'na.fail'), can also be 'na.omit' or
          'na.exclude' with consequences for residuals and fitted
          values - in these cases the weights list will be subsetted to
          remove NAs in the data. It may be necessary to set
          zero.policy to TRUE because this subsetting may create
          no-neighbour observations. Note that only weights lists
          created without using the glist argument to 'nb2listw' may be
          subsetted.

zero.policy: if TRUE assign zero to the lagged value of zones without 
          neighbours, if FALSE (default) assign NA - causing
          'GMerrorsar()' to terminate with an error

return_LL: default FALSE, if TRUE, try to calculate the log likelihood
          of the function for the fitted model values - see details

  method: default '"nlminb"', or optionally a method passed to 'optim'
          to use an alternative optimizer

 control: A list of control parameters. See details in 'optim' or
          'nlminb'.

    pars: starting values for lambda and sigma squared for GMM
          optimisation, if missing (default), approximated from initial
          OLS model as the autocorrelation coefficient corrected for
          weights style and model sigma squared

 verbose: default=FALSE; if TRUE, reports function values during
          optimization.

sparse_method: default "Matrix", can also be "spam" to use spam package
          objects for finding the Jacobian

returnHcov: default FALSE, return the Vo matrix for a spatial Hausman
          test

tol.Hcov: the tolerance for computing the Vo matrix (default=1.0e-10)

 pWOrder: default 250, if returnHcov=TRUE, pass this order to
          'powerWeights' as the power series maximum limit

  object: 'gmsar' object from 'GMerrorsar'

correlation: logical; (default=FALSE), TRUE not available

 Hausman: if TRUE, the results of the Hausman test for error models are
          reported

     ...: 'summary' arguments passed through

_D_e_t_a_i_l_s:

     When the control list is set with care, the function will converge
     to values close to the ML estimator without requiring computation
     of the Jacobian, the most resource-intensive part of ML
     estimation. For moderately sized data sets with hundreds of
     observations, but not many thousands, the Jacobian is computed
     once to give the likelihood of the fitted model, allowing a test
     against the model with no spatial dependence.

     Note that the fitted() function for the output object assumes that
     the response  variable may be reconstructed as the sum of the
     trend, the signal, and the noise (residuals). Since the values of
     the response variable are known, their spatial lags are used to
     calculate signal components (Cressie 1993, p. 564). This differs
     from other software, including GeoDa, which does not use knowledge
     of the response  variable in making predictions for the fitting
     data.

_V_a_l_u_e:

     A list object of class 'gmsar' 

  lambda: simultaneous autoregressive error coefficient

coefficients: GMM coefficient estimates

 rest.se: GMM coefficient standard errors

      s2: GMM residual variance

     SSE: sum of squared GMM errors

parameters: number of parameters estimated

lm.model: the 'lm' object returned when estimating for lambda=0

    call: the call used to create this object

residuals: GMM residuals

lm.target: the 'lm' object returned for the GMM fit

fitted.values: Difference between residuals and response variable

 formula: model formula

 aliased: if not NULL, details of aliased variables

zero.policy: zero.policy for this model

      LL: log likelihood value at computed optimum

      vv: list of internal bigG and litg components for testing
          optimisation surface

  optres: object returned by optimizer

    pars: start parameter values for optimisation

    Hcov: Spatial DGP covariance matrix for Hausman test if available

na.action: (possibly) named vector of excluded or omitted observations
          if non-default na.action argument used

_A_u_t_h_o_r(_s):

     Luc Anselin and Roger Bivand

_R_e_f_e_r_e_n_c_e_s:

     Kelejian, H. H., and Prucha, I. R., 1999. A Generalized Moments
     Estimator for the Autoregressive Parameter in a Spatial Model.
     International Economic Review, 40, pp. 509-533; Cressie, N. A. C.
     1993 _Statistics for spatial data_, Wiley, New York.

_S_e_e _A_l_s_o:

     'optim', 'nlminb', 'errorsarlm'

_E_x_a_m_p_l_e_s:

     data(oldcol)
     COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
      nb2listw(COL.nb, style="W"), method="eigen")
     summary(COL.errW.eig, Hausman=TRUE)
     COL.errW.GM <- GMerrorsar(CRIME ~ INC + HOVAL, data=COL.OLD,
      nb2listw(COL.nb, style="W"), returnHcov=TRUE)
     summary(COL.errW.GM, Hausman=TRUE)
     COL.errW.GM1 <- GMerrorsar(CRIME ~ INC + HOVAL, data=COL.OLD,
      nb2listw(COL.nb, style="W"))
     summary(COL.errW.GM1)
     example(NY_data)
     esar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
      listw=listw_NY, family="SAR", method="full")
     summary(esar1f)
     esar1gm <- GMerrorsar(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
      data=nydata, listw=listw_NY)
     summary(esar1gm)
     esar1gm1 <- GMerrorsar(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
      data=nydata, listw=listw_NY, method="Nelder-Mead")
     summary(esar1gm1)

