Description Usage Arguments Details Value Examples
Draw MCMC samples from the Spatial GLMM with known link function
1 2 3 4 
formula 
A representation of the model in the form

family 
The distribution of the data. The

data 
An optional data frame containing the variables in the model. 
weights 
An optional vector of weights. Number of replicated samples for Gaussian and gamma, number of trials for binomial, time length for Poisson. 
subset 
An optional vector specifying a subset of observations to be used in the fitting process. 
atsample 
A formula in the form 
corrfcn 
Spatial correlation function. See

linkp 
Parameter of the link function. A scalar value. 
phi 
Optional starting value for the MCMC for the
spatial range parameter 
omg 
Optional starting value for the MCMC for the
relative nugget parameter 
kappa 
Optional starting value for the MCMC for the
spatial correlation parameter 
Nout 
Number of MCMC samples to return. This can be a vector for running independent chains. 
Nthin 
The thinning of the MCMC algorithm. 
Nbi 
The burnin of the MCMC algorithm. 
betm0 
Prior mean for beta (a vector or scalar). 
betQ0 
Prior standardised precision (inverse variance) matrix. Can be a scalar, vector or matrix. The first two imply a diagonal with those elements. Set this to 0 to indicate a flat improper prior. 
ssqdf 
Degrees of freedom for the scaled inverse chisquare prior for the partial sill parameter. 
ssqsc 
Scale for the scaled inverse chisquare prior for the partial sill parameter. 
corrpriors 
A list with the components 
corrtuning 
A vector or list with the components 
dispersion 
The fixed dispersion parameter. 
longlat 
How to compute the distance between locations. If

test 
Whether this is a trial run to monitor the acceptance
ratio of the random walk for 
The fourparameter prior for phi
is defined by
propto (phi  phiprior[4])^(phiprior[2]1) * exp(((phiphiprior[4])/phiprior[1])^phiprior[3])
for phi > phiprior[4]. The prior for omg
is similar.
The prior parameters correspond to scale, shape, exponent, and
location. See arXiv:1005.3274
for details of this
distribution.
The GEV (Generalised Extreme Value) link is defined by
mu = 1  \exp[max(0, 1 + nu x)^(1/nu)]
for any real nu. At nu = 0 it reduces to the complementary loglog link.
A list containing the objects MODEL
, DATA
,
FIXED
, MCMC
and call
. The MCMC samples are
stored in the object MCMC
as follows:
z
A matrix containing the MCMC samples for the
spatial random field. Each column is one sample.
mu
A matrix containing the MCMC samples for the
mean response (a transformation of z). Each column is one sample.
beta
A matrix containing the MCMC samples for the
regressor coefficients. Each column is one sample.
ssq
A vector with the MCMC samples for the partial
phi
A vector with the MCMC samples for the spatial
range parameter, if sampled.
omg
A vector with the MCMC samples for the relative
nugget parameter, if sampled.
logLik
A vector containing the value of the
loglikelihood evaluated at each sample.
acc_ratio
The acceptance ratio for the joint update
of the parameters phi
and omg
, if sampled.
sys_time
The total computing time for the MCMC sampling.
Nout
, Nbi
, Nthin
As in input. Used
internally in other functions.
The other objects contain input variables. The object call
contains the function call.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46  ## Not run:
data(rhizoctonia)
### Create prediction grid
predgrid < mkpredgrid2d(rhizoctonia[c("Xcoord", "Ycoord")],
par.x = 100, chull = TRUE, exf = 1.2)
### Combine observed and prediction locations
rhizdata < stackdata(rhizoctonia, predgrid$grid)
##'
### Define the model
corrf < "spherical"
family < "binomial.probit"
kappa < 0
ssqdf < 1
ssqsc < 1
betm0 < 0
betQ0 < .01
phiprior < c(100, 1, 1000, 100) # U(100, 200)
phisc < 3
omgprior < c(2, 1, 1, 0) # Exp(mean = 2)
omgsc < .1
##'
### MCMC sizes
Nout < 100
Nthin < 1
Nbi < 0
### Trial run
emt < mcsglmm(Infected ~ 1, family, rhizdata, weights = Total,
atsample = ~ Xcoord + Ycoord,
Nout = Nout, Nthin = Nthin, Nbi = Nbi,
betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc,
corrpriors = list(phi = phiprior, omg = omgprior),
corrfcn = corrf, kappa = kappa,
corrtuning = list(phi = phisc, omg = omgsc, kappa = 0),
dispersion = 1, test = 10)
### Full run
emc < update(emt, test = FALSE)
emcmc < mcmcmake(emc)
summary(emcmc[, c("phi", "omg", "beta", "ssq")])
plot(emcmc[, c("phi", "omg", "beta", "ssq")])
## End(Not run)

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