SET_MCMC.RdThe spBreg_lag function is an early-release version of the Matlab Spatial Econometrics Toolbox function sar_g.m, using drawing by inversion, and not accommodating heteroskedastic disturbances.
spBreg_lag(formula, data = list(), listw, na.action, Durbin, type,
zero.policy=NULL, control=list())
spBreg_sac(formula, data = list(), listw, listw2=NULL, na.action,
Durbin, type, zero.policy=NULL, control=list())
spBreg_err(formula, data = list(), listw, na.action, Durbin, etype,
zero.policy=NULL, control=list())
# S3 method for MCMC_sar_G
impacts(obj, ..., tr=NULL, listw=NULL, evalues=NULL, Q=NULL)
# S3 method for MCMC_sem_G
impacts(obj, ..., tr=NULL, listw=NULL, evalues=NULL, Q=NULL)
# S3 method for MCMC_sac_G
impacts(obj, ..., tr=NULL, listw=NULL, evalues=NULL, Q=NULL)a symbolic description of the model to be fit. The details
of model specification are given for lm()
an optional data frame containing the variables in the model. By default the variables are taken from the environment which the function is called.
a listw object created for example by nb2listw
a function (default options("na.action")), 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.
default FALSE (spatial lag model); if TRUE, full spatial Durbin model; if a formula object, the subset of explanatory variables to lag
(use the ‘Durbin=’ argument - retained for backwards compatibility only) default "lag", may be set to "mixed"; when "mixed", the lagged intercept is dropped for spatial weights style "W", that is row-standardised weights, but otherwise included; “Durbin” may be used instead of “mixed”
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE (default) assign NA
list of extra control arguments - see section below
A spatial regression object
Arguments passed through to methods in the coda package
A vector of traces of powers of the spatial weights matrix created using trW, for approximate impact measures; if not given, listw must be given for exact measures (for small to moderate spatial weights matrices); the traces must be for the same spatial weights as were used in fitting the spatial regression, and must be row-standardised
vector of eigenvalues of spatial weights matrix for impacts calculations
default NULL, else an integer number of cumulative power series impacts to calculate if tr is given
the desired accuracy of the optimization - passed to optimize() (default=square root of double precision machine tolerance, a larger root may be used needed, see help(boston) for an example)
default NULL, then set to (method != "eigen") internally; use fdHess to compute an approximate Hessian using finite differences when using sparse matrix methods; used to make a coefficient covariance matrix when the number of observations is large; may be turned off to save resources if need be
default FALSE, use fdHess from nlme, if TRUE, use optim to calculate Hessian at optimum
default “optimHess”, may be “nlm” or one of the optim methods
default FALSE; logical value used in the log likelihood function to choose compiled code for computing SSE
default 2; used for preparing the Cholesky decompositions for updating in the Jacobian function
if NULL (default), set to FALSE to use a simplicial decomposition for the sparse Cholesky decomposition and method “Matrix_J”, set to as.logical(NA) for method “Matrix”, if TRUE, use a supernodal decomposition
default 5; highest power of the approximating polynomial for the Chebyshev approximation
default 16; number of random variates
default 30; number of products of random variates matrix and spatial weights matrix
default “MMD”, alternative “RCM”
default 0.1, coefficient value for initial Cholesky decomposition in “spam_update”
default “MC”, used with method “moments”; alternatives “mult” and “moments”, for use if trs is missing, trW
default TRUE, used with method “moments” to compute the Smirnov/Anselin correction term
default TRUE, used with method “moments” to truncate the Smirnov/Anselin correction term
default “LU”, may be “MC”
default 200, as in SE toolbox; the size of the first stage lndet grid; it may be reduced to for example 40
default 2000, as in SE toolbox; the size of the second stage lndet grid
default TRUE; if the method is not “eigen”, use asymmetric covariances rather than numerical Hessian ones if n <= small
default 1500; threshold number of observations for asymmetric covariances when the method is not “eigen”
default NULL, may be used to pass a pre-computed SE toolbox style matrix of coefficients and their lndet values to the "SE_classic" and "SE_whichMin" methods
default FALSE; used in “LU_prepermutate”, note warnings given for lu method
default NULL; may be used to pass a pre-computed vector of eigenvalues
default 1; used to set the sign of the final component to negative if -1 (alpha times ((sigma squared) squared) in Ord (1975) equation B.1).
default “SE_classic”; equivalent to the method argument in lagsarlm
default c(-1, 1); used unmodified or set internally by jacobianSetup
default 2500L; integer total number of draws
default 500L; integer total number of omitted burn-in draws
default 1L; integer thinning proportion
default FALSE; inverse of quiet argument in lagsarlm
default NULL; not yet in use, precomputed matrix of log determinants
a list with the following components:
default FALSE; use Metropolis or griddy Gibbs
default NULL; values of the betas variance-covariance matrix, set to diag(k)*1e+12 if NULL
default NULL; values of the betas set to 0 if NULL
default 0.5; value of the autoregressive coefficient
default 1; value of the residual variance
default 0; informative Gamma(nu,d0) prior on sige
default 0; informative Gamma(nu,d0) prior on sige
default 1.01; parameter for beta(a1,a2) prior on rho
default 1.01; parameter for beta(a1,a2) prior on rho
default 0.2; initial tuning parameter for M-H sampling
default TRUE; include sige in lambda griddy Gibbs update
default 0.2; initial tuning parameter for M-H sampling
default 0.2; initial tuning parameter for M-H sampling
LeSage J and RK Pace (2009) Introduction to Spatial Econometrics. CRC Press, Boca Raton.
#require("spdep", quietly=TRUE)
data(oldcol, package="spdep")
lw <- spdep::nb2listw(COL.nb, style="W")
require("coda", quietly=TRUE)
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw)
print(summary(COL.err.Bayes))
#>
#> Iterations = 501:2500
#> Thinning interval = 1
#> Number of chains = 1
#> Sample size per chain = 2000
#>
#> 1. Empirical mean and standard deviation for each variable,
#> plus standard error of the mean:
#>
#> Mean SD Naive SE Time-series SE
#> (Intercept) 59.6663 6.99423 0.156396 0.156396
#> INC -0.9421 0.39894 0.008921 0.008921
#> HOVAL -0.3011 0.09984 0.002233 0.002233
#> lambda 0.5664 0.15362 0.003435 0.003435
#> sige 115.0482 25.96435 0.580581 0.622638
#>
#> 2. Quantiles for each variable:
#>
#> 2.5% 25% 50% 75% 97.5%
#> (Intercept) 45.6439 55.3852 60.1164 64.5665 72.1349
#> INC -1.7360 -1.2047 -0.9445 -0.6803 -0.1379
#> HOVAL -0.4977 -0.3683 -0.3010 -0.2346 -0.1070
#> lambda 0.2335 0.4717 0.5773 0.6758 0.8309
#> sige 74.4245 96.7013 111.9152 129.2463 176.8133
#>
print(raftery.diag(COL.err.Bayes, r=0.01))
#>
#> Quantile (q) = 0.025
#> Accuracy (r) = +/- 0.01
#> Probability (s) = 0.95
#>
#> Burn-in Total Lower bound Dependence
#> (M) (N) (Nmin) factor (I)
#> (Intercept) 3 1052 937 1.120
#> INC 2 969 937 1.030
#> HOVAL 3 1052 937 1.120
#> lambda 3 1010 937 1.080
#> sige 2 930 937 0.993
#>
if (FALSE) {
ev <- eigenw(lw)
W <- as(lw, "CsparseMatrix")
trMatc <- trW(W, type="mult")
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
control=list(prior=list(lambdaMH=TRUE)))
print(summary(COL.err.Bayes))
print(raftery.diag(COL.err.Bayes, r=0.01))
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=TRUE)
print(summary(COL.err.Bayes))
print(summary(impacts(COL.err.Bayes)))
print(raftery.diag(COL.err.Bayes, r=0.01))
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=TRUE, control=list(prior=list(lambdaMH=TRUE)))
print(summary(COL.err.Bayes))
print(summary(impacts(COL.err.Bayes)))
print(raftery.diag(COL.err.Bayes, r=0.01))
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=~INC)
print(summary(COL.err.Bayes))
print(summary(impacts(COL.err.Bayes)))
print(raftery.diag(COL.err.Bayes, r=0.01))
set.seed(1)
COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=~INC, control=list(prior=list(lambdaMH=TRUE)))
print(summary(COL.err.Bayes))
print(summary(impacts(COL.err.Bayes)))
print(raftery.diag(COL.err.Bayes, r=0.01))
set.seed(1)
COL.sacW.B0 <- spBreg_sac(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=FALSE, control=list(ndraw=1500L, nomit=500L))
print(summary(COL.sacW.B0))
print(summary(impacts(COL.sacW.B0, tr=trMatc), zstats=TRUE, short=TRUE))
set.seed(1)
COL.sacW.B1 <- spBreg_sac(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
Durbin=TRUE, control=list(ndraw=1500L, nomit=500L))
print(summary(COL.sacW.B1))
print(summary(impacts(COL.sacW.B1, tr=trMatc), zstats=TRUE, short=TRUE))
set.seed(1)
COL.lag.Bayes <- spBreg_lag(CRIME ~ INC + HOVAL, data=COL.OLD,
listw=lw)
print(summary(COL.lag.Bayes))
print(summary(impacts(COL.lag.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
print(summary(impacts(COL.lag.Bayes, evalues=ev), short=TRUE, zstats=TRUE))
set.seed(1)
COL.D0.Bayes <- spBreg_lag(CRIME ~ INC + HOVAL, data=COL.OLD,
listw=lw, Durbin=TRUE)
print(summary(COL.D0.Bayes))
print(summary(impacts(COL.D0.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
set.seed(1)
COL.D1.Bayes <- spBreg_lag(CRIME ~ DISCBD + INC + HOVAL, data=COL.OLD,
listw=lw, Durbin= ~ INC)
print(summary(COL.D1.Bayes))
print(summary(impacts(COL.D1.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
#data(elect80, package="spData")
#lw <- spdep::nb2listw(e80_queen, zero.policy=TRUE)
#el_ml <- lagsarlm(log(pc_turnout) ~ log(pc_college) + log(pc_homeownership)
# + log(pc_income), data=elect80, listw=lw, zero.policy=TRUE, method="LU")
#print(summary(el_ml))
#set.seed(1)
#el_B <- spBreg_lag(log(pc_turnout) ~ log(pc_college) + log(pc_homeownership)
# + log(pc_income), data=elect80, listw=lw, zero.policy=TRUE)
#print(summary(el_B))
#print(el_ml$timings)
#print(attr(el_B, "timings"))
}