spdep (version 0.6-9)

MCMCsamp: MCMC sample from fitted spatial regression

Description

The MCMCsamp method uses rwmetrop, a random walk Metropolis algorithm, from LearnBayes to make MCMC samples from fitted maximum likelihood spatial regression models.

Usage

MCMCsamp(object, mcmc = 1L, verbose = NULL, ...)
"MCMCsamp"(object, mcmc = 1L, verbose = NULL, ..., burnin = 0L, scale=1, listw, control = list())
"MCMCsamp"(object, mcmc = 1L, verbose = NULL, ..., burnin=0L, scale=1, listw, listw2=NULL, control=list())

Arguments

object

A spatial regression model object fitted by maximum likelihood with spautolm

mcmc

The number of MCMC iterations after burnin

verbose

default NULL, use global option value; if TRUE, reports progress

...

Arguments passed through

burnin

The number of burn-in iterations for the sampler

scale

a positive scale parameter

listw, listw2

listw objects created for example by nb2listw; should be the same object(s) used for fitting the model

control

list of extra control arguments - see spautolm

Value

“mcmc” suited to coda, with attributes: “accept” acceptance rate; “type” input ML fitted model type “SAR”, “CAR”, “SMA”, “lag”, “mixed”, “error”, “sac”, “sacmixed”; “timings” run times

References

Jim Albert (2007) Bayesian Computation with R, Springer, New York, pp. 104-105.

See Also

rwmetrop, spautolm, lagsarlm, errorsarlm, sacsarlm

Examples

example(NY_data)
## Not run: 
# esar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, family="SAR", method="eigen")
# summary(esar1f)
# res <- MCMCsamp(esar1f, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# esar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, weights=POP8, family="SAR", method="eigen")
# summary(esar1fw)
# res <- MCMCsamp(esar1fw, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ecar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, family="CAR", method="eigen")
# summary(ecar1f)
# res <- MCMCsamp(ecar1f, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# esar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, weights=POP8, family="SAR", method="eigen")
# summary(esar1fw)
# res <- MCMCsamp(esar1fw, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ecar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, weights=POP8, family="CAR", method="eigen")
# summary(ecar1fw)
# res <- MCMCsamp(ecar1fw, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ## End(Not run)
esar0 <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
 listw=listw_NY)
summary(esar0)
res <- MCMCsamp(esar0, mcmc=5000, burnin=500, listw=listw_NY)
summary(res)
## Not run: 
# esar0w <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, weights=POP8)
# summary(esar0)
# res <- MCMCsamp(esar0w, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# esar1 <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, etype="emixed")
# summary(esar1)
# res <- MCMCsamp(esar1, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# lsar0 <- lagsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY)
# summary(lsar0)
# res <- MCMCsamp(lsar0, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# lsar1 <- lagsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, type="mixed")
# summary(lsar1)
# res <- MCMCsamp(lsar1, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ssar0 <- sacsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY)
# summary(ssar0)
# res <- MCMCsamp(ssar0, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ssar1 <- sacsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
#  listw=listw_NY, type="sacmixed")
# summary(ssar1)
# res <- MCMCsamp(ssar1, mcmc=5000, burnin=500, listw=listw_NY)
# summary(res)
# ## End(Not run)