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 = TRUE, control = list(), verbose=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

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

return_LL: default TRUE, if FALSE, do not try to calculate the log
          likelihood of the function for the fitted model values - see
          details

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

_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

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', '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)
     COL.errW.GM <- GMerrorsar(CRIME ~ INC + HOVAL, data=COL.OLD, nb2listw(COL.nb, style="W"))
     summary(COL.errW.GM)
     data(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)

