Description Usage Arguments Details Value Author(s) References See Also Examples
The function spMvGLM
fits multivariate Bayesian
generalized linear spatial regression models. Given a set of knots,
spMvGLM
will also fit a predictive process model (see references below).
1 2 3 4 
formula 
a list of q symbolic regression model descriptions to be fit. See example below. 
family 
currently only supports 
weights 
an optional n x q matrix of weights
to be used in the fitting process. The order of the
columns correspond to the univariate models in the formula list. Weights correspond to number of trials and offset for
each location for the 
data 
an optional data frame containing the variables in the
model. If not found in data, the variables are taken from

coords 
an n x 2 matrix of the observation coordinates in R^2 (e.g., easting and northing). 
knots 
either a m x 2 matrix of the
predictive process knot coordinates in R^2 (e.g.,
easting and northing) or a vector of length two or three with the
first and second elements recording the number of columns and rows in
the desired knot grid. The third, optional, element sets the offset of
the outermost knots from the extent of the 
starting 
a list with each tag corresponding to a parameter name. Valid tags are 
tuning 
a list with tags 
priors 
a list with each tag corresponding to a
parameter name. Valid tags are 
cov.model 
a quoted keyword that specifies the covariance
function used to model the spatial dependence structure among the
observations. Supported covariance model key words are:

amcmc 
a list with tags 
n.samples 
the number of MCMC iterations. This argument is
ignored if 
verbose 
if 
n.report 
the interval to report Metropolis sampler acceptance and MCMC progress. 
... 
currently no additional arguments. 
If a binomial
model is specified the response vector is the
number of successful trials at each location and weights
is the
total number of trials at each location.
For a poisson
specification, the weights
vector is the
count offset, e.g., population, at each location. This differs from
the glm
offset
argument which is passed as the
log of this value.
A nonspatial model is fit when coords
is not specified. See
example below.
An object of class spMvGLM
, which is a list with the following
tags:
coords 
the n x 2 matrix specified by

knot.coords 
the m x 2 matrix as specified by 
p.beta.theta.samples 
a 
acceptance 
the Metropolis sampler
acceptance rate. If 
acceptance.w 
if this is a nonpredictive process model and

acceptance.w.knots 
if this is a predictive process model and 
p.w.knots.samples 
a matrix that holds samples from the posterior distribution of the knots' spatial random effects. The rows of this matrix correspond to the q x m knot locations and the columns are the posterior samples. This is only returned if a predictive process model is used. 
p.w.samples 
a matrix that holds samples from the posterior distribution of the locations' spatial random effects. The rows of this matrix correspond to the q x n point observations and the columns are the posterior samples. 
The return object might include additional data used for subsequent prediction and/or model fit evaluation.
Andrew O. Finley [email protected]du,
Sudipto Banerjee [email protected]
Finley, A.O., S. Banerjee, and R.E. McRoberts. (2008) A Bayesian approach to quantifying uncertainty in multisource forest area estimates. Environmental and Ecological Statistics, 15:241–258.
Banerjee, S., A.E. Gelfand, A.O. Finley, and H. Sang. (2008) Gaussian Predictive Process Models for Large Spatial Datasets. Journal of the Royal Statistical Society Series B, 70:825–848.
Finley, A.O., H. Sang, S. Banerjee, and A.E. Gelfand. (2009) Improving the performance of predictive process modeling for large datasets. Computational Statistics and Data Analysis, 53:28732884.
Finley, A.O., S. Banerjee, and A.E. Gelfand. (2015) spBayes for large univariate and multivariate pointreferenced spatiotemporal data models. Journal of Statistical Software, 63:1–28. http://www.jstatsoft.org/v63/i13.
Banerjee, S., Carlin, B.P., and Gelfand, A.E. (2004). Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC Press, Boca Raton, Fla.
Roberts G.O. and Rosenthal J.S. (2006) Examples of Adaptive MCMC. http://probability.ca/jeff/ftpdir/adaptex.pdf Preprint.
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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118  ## Not run:
library(MBA)
##Some useful functions
rmvn < function(n, mu=0, V = matrix(1)){
p < length(mu)
if(any(is.na(match(dim(V),p)))){stop("Dimension problem!")}
D < chol(V)
t(matrix(rnorm(n*p), ncol=p)%*%D + rep(mu,rep(n,p)))
}
set.seed(1)
##Generate some data
n < 25 ##number of locations
q < 2 ##number of outcomes at each location
nltr < q*(q+1)/2 ##number of triangular elements in the crosscovariance matrix
coords < cbind(runif(n,0,1), runif(n,0,1))
##Parameters for the bivariate spatial random effects
theta < rep(3/0.5,q)
A < matrix(0,q,q)
A[lower.tri(A,TRUE)] < c(1,1,0.25)
K < A%*%t(A)
Psi < diag(0,q)
C < mkSpCov(coords, K, Psi, theta, cov.model="exponential")
w < rmvn(1, rep(0,nrow(C)), C)
w.1 < w[seq(1,length(w),q)]
w.2 < w[seq(2,length(w),q)]
##Covariate portion of the mean
x.1 < cbind(1, rnorm(n))
x.2 < cbind(1, rnorm(n))
x < mkMvX(list(x.1, x.2))
B.1 < c(1,1)
B.2 < c(1,1)
B < c(B.1, B.2)
weight < 10 ##i.e., trials
p < 1/(1+exp((x%*%B+w)))
y < rbinom(n*q, size=rep(weight,n*q), prob=p)
y.1 < y[seq(1,length(y),q)]
y.2 < y[seq(2,length(y),q)]
##Call spMvLM
fit < glm((y/weight)~x1, weights=rep(weight, n*q), family="binomial")
beta.starting < coefficients(fit)
beta.tuning < t(chol(vcov(fit)))
A.starting < diag(1,q)[lower.tri(diag(1,q), TRUE)]
n.batch < 100
batch.length < 50
n.samples < n.batch*batch.length
starting < list("beta"=beta.starting, "phi"=rep(3/0.5,q), "A"=A.starting, "w"=0)
tuning < list("beta"=beta.tuning, "phi"=rep(1,q), "A"=rep(0.1,length(A.starting)),
"w"=0.5)
priors < list("beta.Flat", "phi.Unif"=list(rep(3/0.75,q), rep(3/0.25,q)),
"K.IW"=list(q+1, diag(0.1,q)))
m.1 < spMvGLM(list(y.1~x.11, y.2~x.21),
coords=coords, weights=matrix(weight,n,q),
starting=starting, tuning=tuning, priors=priors,
amcmc=list("n.batch"=n.batch,"batch.length"=batch.length,"accept.rate"=0.43),
cov.model="exponential", n.report=25)
burn.in < 0.75*n.samples
sub.samps < burn.in:n.samples
print(summary(window(m.1$p.beta.theta.samples, start=burn.in))$quantiles[,c(3,1,5)])
beta.hat < t(m.1$p.beta.theta.samples[sub.samps,1:length(B)])
w.hat < m.1$p.w.samples[,sub.samps]
p.hat < 1/(1+exp((x%*%beta.hat+w.hat)))
y.hat < apply(p.hat, 2, function(x){rbinom(n*q, size=rep(weight, n*q), prob=p)})
y.hat.mu < apply(y.hat, 1, mean)
##Unstack to get each response variable fitted values
y.hat.mu.1 < y.hat.mu[seq(1,length(y.hat.mu),q)]
y.hat.mu.2 < y.hat.mu[seq(2,length(y.hat.mu),q)]
##Take a look
par(mfrow=c(2,2))
surf < mba.surf(cbind(coords,y.1),no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(surf, main="Observed y.1 positive trials")
contour(surf, add=TRUE)
points(coords)
zlim < range(surf[["z"]], na.rm=TRUE)
surf < mba.surf(cbind(coords,y.hat.mu.1),no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(surf, zlim=zlim, main="Fitted y.1 positive trials")
contour(surf, add=TRUE)
points(coords)
surf < mba.surf(cbind(coords,y.2),no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(surf, main="Observed y.2 positive trials")
contour(surf, add=TRUE)
points(coords)
zlim < range(surf[["z"]], na.rm=TRUE)
surf < mba.surf(cbind(coords,y.hat.mu.2),no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(surf, zlim=zlim, main="Fitted y.2 positive trials")
contour(surf, add=TRUE)
points(coords)
## End(Not run)

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