ST.CARsepspatial: Fit a spatio-temporal generalised linear mixed model to data,...

In duncanplee/CARBayesST: Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data

Description

Fit a spatio-temporal generalised linear mixed model to areal unit data, where the response variable can be binomial or Poisson. The linear predictor is modelled by known covariates and two sets of random effects. These include a common temporal main effect, and separate time period specific spatial effects with a common spatial dependence parameter but separate variance parameters. Each component is modelled by the conditional autoregressive (CAR) prior proposed by Leroux et al. (2000). Further details are given in Napier et al. (2016) and in the vignette accompanying this package. Inference is conducted in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation.

Usage

ST.CARsepspatial(formula, family, data=NULL,  trials=NULL, W, burnin, n.sample,
thin=1, prior.mean.beta=NULL, prior.var.beta=NULL, prior.tau2=NULL, rho.S=NULL, 
rho.T=NULL, MALA=FALSE, verbose=TRUE)

Arguments

formula	A formula for the covariate part of the model using the syntax of the lm() function. Offsets can be included here using the offset() function. The response and each covariate should be vectors of length (KN)*1, where K is the number of spatial units and N is the number of time periods. Each vector should be ordered so that the first K data points are the set of all K spatial locations at time 1, the next K are the set of spatial locations for time 2 and so on. The response must NOT contain missing (NA) values.
family	One of either "binomial" or "poisson", which respectively specify a binomial likelihood model with a logistic link function, or a Poisson likelihood model with a log link function.
data	An optional data.frame containing the variables in the formula.
trials	A vector the same length and in the same order as the response containing the total number of trials for each area and time period. Only used if family="binomial".
W	A non-negative K by K neighbourhood matrix (where K is the number of spatial units). Typically a binary specification is used, where the jkth element equals one if areas (j, k) are spatially close (e.g. share a common border) and is zero otherwise. The matrix can be non-binary, but each row must contain at least one non-zero entry.
burnin	The number of MCMC samples to discard as the burn-in period.
n.sample	The number of MCMC samples to generate.
thin	The level of thinning to apply to the MCMC samples to reduce their temporal autocorrelation. Defaults to 1 (no thinning).
prior.mean.beta	A vector of prior means for the regression parameters beta (Gaussian priors are assumed). Defaults to a vector of zeros.
prior.var.beta	A vector of prior variances for the regression parameters beta (Gaussian priors are assumed). Defaults to a vector with values 100000.
prior.tau2	The prior shape and scale in the form of c(shape, scale) for an Inverse-Gamma(shape, scale) prior for tau2. Defaults to c(1, 0.01).
rho.S	The value in the interval [0, 1] that the spatial dependence parameter rho.S is fixed at if it should not be estimated. If this arugment is NULL then rho.S is estimated in the model.
rho.T	The value in the interval [0, 1] that the temporal dependence parameter rho.T is fixed at if it should not be estimated. If this arugment is NULL then rho.T is estimated in the model.
MALA	Logical, should the function use Metropolis adjusted Langevin algorithm (MALA) updates (TRUE) or simple random walk (FALSE, default) updates for the regression parameters. Not applicable if family="gaussian".
verbose	Logical, should the function update the user on its progress.

Value

summary.results	A summary table of the parameters.
samples	A list containing the MCMC samples from the model.
fitted.values	A vector of fitted values for each area and time period.
residuals	A matrix with 2 columns where each column is a type of residual and each row relates to an area and time period. The types are "response" (raw), and "pearson".
modelfit	Model fit criteria including the Deviance Information Criterion (DIC) and its corresponding estimated effective number of parameters (p.d), the Log Marginal Predictive Likelihood (LMPL), the Watanabe-Akaike Information Criterion (WAIC) and its corresponding estimated number of effective parameters (p.w), and the loglikelihood.
accept	The acceptance probabilities for the parameters.
localised.structure	NULL, for compatability with the other models.
formula	The formula (as a text string) for the response, covariate and offset parts of the model.
model	A text string describing the model fit.
X	The design matrix of covariates.

Author(s)

Gary Napier

References

Napier, G, D. Lee, C. Robertson, A. Lawson, and K. Pollock (2016). A model to estimate the impact of changes in MMR vaccination uptake on inequalities in measles susceptibility in Scotland, Statistical Methods in Medical Research, 25, 1185-1200.

Examples

#################################################
#### Run the model on simulated data on a lattice
#################################################
#### set up the regular lattice    
x.easting <- 1:10
x.northing <- 1:10
Grid <- expand.grid(x.easting, x.northing)
K <- nrow(Grid)
N <- 5
N.all <- N * K
  
        
#### set up spatial neighbourhood matrix W
distance <- as.matrix(dist(Grid))
W <-array(0, c(K,K))
W[distance==1] <-1 	


#### Create the spatial covariance matrix
Q.W <- 0.99 * (diag(apply(W, 2, sum)) - W) + 0.01 * diag(rep(1,K))
Q.W.inv <- solve(Q.W)
  
           
#### Simulate the elements in the linear predictor and the data
x <- rnorm(n=N.all, mean=0, sd=1)
beta <- 0.1

phi1 <- mvrnorm(n=1, mu=rep(0,K), Sigma=(0.01 * Q.W.inv))
phi2 <- mvrnorm(n=1, mu=rep(0,K), Sigma=(0.01 * Q.W.inv))
phi3 <- mvrnorm(n=1, mu=rep(0,K), Sigma=(0.01 * Q.W.inv))
phi4 <- mvrnorm(n=1, mu=rep(0,K), Sigma=(0.05 * Q.W.inv))
phi5 <- mvrnorm(n=1, mu=rep(0,K), Sigma=(0.05 * Q.W.inv))
  
delta <- c(0, 0.5, 0, 0.5, 0)
phi.long <- c(phi1, phi2, phi3, phi4, phi5)
delta.long <- kronecker(delta, rep(1,K))
LP <- 4 +  x * beta + phi.long +  delta.long
mean <- exp(LP)
Y <- rpois(n=N.all, lambda=mean)
  
                
#### Run the model
## Not run: model <- ST.CARsepspatial(formula=Y~x, family="poisson", W=W, burnin=10000, 
n.sample=50000)
## End(Not run)


#### Toy example for checking
model <- ST.CARsepspatial(formula=Y~x, family="poisson", W=W, burnin=10, 
n.sample=50)

duncanplee/CARBayesST documentation built on May 29, 2021, 7:35 a.m.