spMisalignGLM: Function for fitting multivariate generalized linear Bayesian spatial regression models to misaligned data

Description

The function spMisalignGLM fits Gaussian multivariate Bayesian generalized linear spatial regression models to misaligned data.

Usage

spMisalignGLM(formula, family="binomial", weights, data = parent.frame(), coords,  starting, tuning, priors, cov.model, amcmc, n.samples, verbose=TRUE, n.report=100, ...)

Arguments

formula

a list of $q$ symbolic regression models to be fit. See example below.

family

currently only supports binomial and poisson data using the logit and log link functions, respectively.

weights

an optional list of weight vectors associated with each model in the formula list. Weights correspond to number of trials and offset for each location for the binomial and poisson family, respectively.

data

an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which spMisalignGLM is called.

coords

a list of $q$ $n_i x 2$ matrices of the observation coordinates in $R^2$ (e.g., easting and northing) where $i=(1,2,\ldots,q)$.

starting

a list with tags corresponding to A, phi, and nu. The value portion of each tag is a vector that holds the parameter's starting values. A is of length $q(q+1)/2$ and holds the lower-triangle elements in column major ordering of the Cholesky square root of the spatial cross-covariance matrix $K=AA'$. phi and nu are of length $q$.

tuning

a list with tags A, phi, and nu. The value portion of each tag defines the variance of the Metropolis sampler Normal proposal distribution. A is of length $q(q+1)/2$ and phi and nu are of length $q$.

priors

a list with each tag corresponding to a parameter name. Valid tags are beta.flat, beta.norm, K.iw, phi.unif, and nu.unif. If the regression coefficients are each assumed to follow a Normal distribution, i.e., beta.norm, then mean and variance hyperparameters are passed as the first and second list elements, respectively. If beta is assumed flat then no arguments are passed. The default is a flat prior. The spatial cross-covariance matrix $K=AA'$ is assumed to follow an inverse-Wishart distribution, whereas the spatial decay phi and smoothness nu parameters are assumed to follow Uniform distributions. The hyperparameters of the inverse-Wishart are passed as a list of length two, with the first and second elements corresponding to the $df$ and $qxq$ scale matrix, respectively. The hyperparameters of the Uniform are also passed as a list of vectors with the first and second list elements corresponding to the lower and upper support, respectively.

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: "exponential", "matern", "spherical", and "gaussian". See below for details.

amcmc

a list with tags n.batch, batch.length, and accept.rate. Specifying this argument invokes an adaptive MCMC sampler see Roberts and Rosenthal (2007) for an explanation.

n.samples

the number of MCMC iterations. This argument is ignored if amcmc is specified.

verbose

if TRUE, model specification and progress of the sampler is printed to the screen. Otherwise, nothing is printed to the screen.

n.report

the interval to report Metropolis acceptance and MCMC progress.

...

currently no additional arguments.

Value

p.beta.theta.samples: a coda object of posterior samples for the defined parameters.
acceptance: the Metropolis sampler acceptance rate. If amcmc is used then this will be a matrix of each parameter's acceptance rate at the end of each batch. Otherwise, the sampler is a Metropolis with a joint proposal of all parameters.
acceptance.w: if amcmc is used then this will be a matrix of the Metropolis sampler acceptance rate for each location's spatial random effect.
p.w.samples: a matrix that holds samples from the posterior distribution of the locations' spatial random effects. Posterior samples are organized with the first response variable $n_1$ locations held in rows $1,\ldots,n_1$ rows, then the next response variable samples held in the $(n_1+1),\ldots,(n_1+n_2)$, etc.

Details

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.

References

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. 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.

Finley, A.O., S. Banerjee, and B.D. Cook. (2014) Bayesian hierarchical models for spatially misaligned data. Methods in Ecology and Evolution, 5:514--523. 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, 53:2873--2884. Finley, A.O., S. Banerjee, A.R. Ek, and R.E. McRoberts. (2008) Bayesian multivariate process modeling for prediction of forest attributes. Journal of Agricultural, Biological, and Environmental Statistics, 13:60--83.

Examples

Run this code

## Not run: 
# 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 <- 100 ##number of locations
# q <- 3 ##number of outcomes at each location
# nltr <- q*(q+1)/2 ##number of triangular elements in the cross-covariance matrix
# 
# coords <- cbind(runif(n,0,1), runif(n,0,1))
# 
# ##parameters for generating a multivariate spatial GP covariance matrix
# theta <- rep(3/0.5,q) ##spatial decay
# 
# A <- matrix(0,q,q)
# A[lower.tri(A,TRUE)] <- c(1,1,-1,1,0.5,0.25)
# K <- A%*%t(A)
# K ##spatial cross-covariance
# cov2cor(K) ##spatial cross-correlation
# 
# C <- mkSpCov(coords, K, diag(0,q), theta, cov.model="exponential")
# 
# w <- rmvn(1, rep(0,nrow(C)), C) ##spatial random effects
# 
# w.a <- w[seq(1,length(w),q)]
# w.b <- w[seq(2,length(w),q)]
# w.c <- w[seq(3,length(w),q)]
# 
# ##covariate portion of the mean
# x.a <- cbind(1, rnorm(n))
# x.b <- cbind(1, rnorm(n))
# x.c <- cbind(1, rnorm(n))
# x <- mkMvX(list(x.a, x.b, x.c))
# 
# B.1 <- c(1,-1)
# B.2 <- c(-1,1)
# B.3 <- c(-1,-1)
# B <- c(B.1, B.2, B.3)
# 
# y <- rpois(nrow(C), exp(x%*%B+w))
# 
# y.a <- y[seq(1,length(y),q)]
# y.b <- y[seq(2,length(y),q)]
# y.c <- y[seq(3,length(y),q)]
# 
# ##subsample to make spatially misaligned data
# sub.1 <- 1:50
# y.1 <- y.a[sub.1]
# w.1 <- w.a[sub.1]
# coords.1 <- coords[sub.1,]
# x.1 <- x.a[sub.1,]
# 
# sub.2 <- 25:75
# y.2 <- y.b[sub.2]
# w.2 <- w.b[sub.2]
# coords.2 <- coords[sub.2,]
# x.2 <- x.b[sub.2,]
# 
# sub.3 <- 50:100
# y.3 <- y.c[sub.3]
# w.3 <- w.c[sub.3]
# coords.3 <- coords[sub.3,]
# x.3 <- x.c[sub.3,]
# 
# ##call spMisalignGLM
# q <- 3
# A.starting <- diag(1,q)[lower.tri(diag(1,q), TRUE)]
# 
# n.batch <- 200
# batch.length <- 25
# n.samples <- n.batch*batch.length
# 
# starting <- list("beta"=rep(0,length(B)), "phi"=rep(3/0.5,q), "A"=A.starting, "w"=0)
#                  
# tuning <- list("beta"=rep(0.1,length(B)), "phi"=rep(1,q), "A"=rep(0.1,length(A.starting)), "w"=1)
# 
# priors <- list("phi.Unif"=list(rep(3/0.75,q), rep(3/0.25,q)),
#                "K.IW"=list(q+1, diag(0.1,q)),  rep(0.1,q))
# 
# m.1 <- spMisalignGLM(list(y.1~x.1-1, y.2~x.2-1, y.3~x.3-1), family="poisson",
#                      coords=list(coords.1, coords.2, coords.3),
#                      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=10)
# 
# burn.in <- floor(0.75*n.samples)
# 
# plot(m.1$p.beta.theta.samples, density=FALSE)
# 
# ##predict for all locations, i.e., observed and not observed
# out <- spPredict(m.1, start=burn.in, thin=10, pred.covars=list(x.a, x.b, x.c), 
#                  pred.coords=list(coords, coords, coords))
# 
# ##summary and check
# quants <- function(x){quantile(x, prob=c(0.5,0.025,0.975))}
# 
# y.hat <- apply(out$p.y.predictive.samples, 1, quants)
# 
# ##unstack and plot
# y.a.hat <- y.hat[,1:n]
# y.b.hat <- y.hat[,(n+1):(2*n)]
# y.c.hat <- y.hat[,(2*n+1):(3*n)]
# 
# par(mfrow=c(1,3))
# plot(y.a ,y.a.hat[1,], xlab="Observed y.a", ylab="Fitted & predicted y.a")
# plot(y.b, y.b.hat[1,], xlab="Observed y.b", ylab="Fitted & predicted y.b")
# plot(y.c, y.c.hat[1,], xlab="Observed y.c", ylab="Fitted & predicted y.c")
# 
#   ## End(Not run)

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