| glsm.mcmc {geoRglm} | R Documentation |
This function performs conditional simulation (by MCMC) in a generalised linear spatial model for fixed parameters.
glsm.mcmc(geodata, coords = geodata$coords, data = geodata$data,
units.m = "default", model, mcmc.input, messages)
geodata |
a list containing elements coords and
data as described next.
Typically an object of the class "geodata" - a geoR data set.
If not provided the arguments coords and data must be provided instead.
The list may also contain an argument units.m as described
below. |
coords |
an n x 2 matrix, each row containing Euclidean
coordinates of the n data locations. By default it takes the
element coords of the argument geodata. |
data |
a vector with data values. By default it takes the
element data of the argument geodata. |
units.m |
n-dimensional vector of observation times for the data. By default (units.m = "default"),
it takes geodata$units.m in case this exist and else a vector of 1's. |
model |
defines the model components. Either an object of
class likGLSM; typically output from
likfit.glsm, or a list containing
the arguments :
|
mcmc.input |
input parameter for the MCMC algorithm. It can take an output from mcmc.control or
a list with elements as for the arguments in
mcmc.control. See documentation for
mcmc.control.ATTENTION: the argument S.scale is necessary while
all the others have default values. |
messages |
logical. Indicates whether or not status messages are printed on the screen (or other output device) while the function is running. |
For simulating the conditional distribution of S given y, the Langevin-Hastings algorithm with the parametrisation in Papaspilliopoulus, Roberts and Skold (2003) is used. This algorithm is a Metropolis-Hastings algorithm, where the proposal distribution uses gradient information from the log-posterior distribution.
The proposal variance (called S.scale; see mcmc.control)
for the algorithm needs to be scaled
such that approximately 60 percent of the proposals are accepted. We
also recommend that the user to studies plots of the autocorrelations.
A list with the following components:
simulations |
an n x n.sim matrix with n.sim being the number of MCMC simulations. containing S_i. Each column corresponds to a conditional simulation of the conditional distribution of S_i at the data locations. |
acc.rate |
matrix with acceptance rates from MCMC. Only returned when no prediction locations are given. |
model |
Information about the model parameters, link function and error distribution family used. |
geodata |
Information about the data. |
call |
the function call. |
Ole F. Christensen OleF.Christensen@agrsci.dk,
Paulo J. Ribeiro Jr. Paulo.Ribeiro@est.ufpr.br.
O. Papaspiliopoulus and G. O. Roberts and M. Skold (2003). Non-centered parameterizations for hierarchical models and data augmentation. Bayesian statistics 7 (eds. J. M. Bernardo, S. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West), Oxford University Press, 307-326.
Further information about geoRglm can be found at:
http://www.daimi.au.dk/~olefc/geoRglm.
binom.krige for prediction with fixed parameters in the
Binomial-normal model, pois.krige for prediction with fixed parameters in the
Poisson normal model.
if(!exists(".Random.seed", envir=.GlobalEnv, inherits = FALSE)) set.seed(1234)
data(b50)
test <- glsm.mcmc(b50, model = list(family="binomial",
cov.pars = c(1,1), beta = c(1,0), trend =~ rnorm(50),
cov.model="spherical", nugget=0.3),
mcmc.input = mcmc.control(S.scale = 0.2, thin = 1))
## visulalising the MCMC output using the coda package
test.coda <- create.mcmc.coda(test, mcmc.input = list(thin = 1))
## Not run:
plot(test.coda)
autocorr.plot(test.coda)
## End(Not run)