georamps(fixed, random, correlation, data, subset, weights,
variance = list(fixed = ~ 1, random = ~ 1, spatial = ~ 1),
aggregate = list(grid = NULL, blockid = ""),
control = ramps.control(...), contrasts = NULL, ...)"formula" object describing the main effects in the mean structure of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right.~ 1 | g, specifying random intercepts for groups defined by the factor g. Several grouping variables may be simultaneously specified, separated by the * operator, as in ~ 'corSpatial' object describing the spatial correlation structure. See the corClasses documentation for a listing of the available structures.data that should be used in the fit. This can be a logical vector, or a numerical vector indicating which observation numbers are to be included, or a character vector of the row names to beaggregate argument below).~ g where g defines a grouping factor for the following elements: fixed for measurement error variances; random for random effects error variances; agrid a data frame of coordinates to use for Monte Carlo integration over geographic blocks at which areal measurements are available; and blockid a character string specifying the column by which to merramps.control documentation for details.contrasts.arg of model.matrix.'ramps' containing the following elements:'mcmc'"} object of monitored model parameters with variable labels in the column names and MCMC iteration numbers in the row names.}
item{z}{code{'mcmc'} object of monitored latent spatial parameters with variable labels in the column names and MCMC iteration numbers in the row names.}
item{loglik}{data log-likelihood at each MCMC iteration.}
item{evals}{total number of evaluations of the slice sampling routine.}
item{call}{the matched function call to code{georamps}.}
item{y}{response vector.}
item{xmat}{design matrix for the main effects.}
item{terms}{the code{'terms'} object for code{xmat}.}
item{xlevels}{list of the factor levels for code{xmat}.}
item{etype}{grouping factor for the measurement error variances.}
item{weights}{weights used in the fitting process.}
item{kmat}{matrix for mapping the spatial parameters to the observed data.}
item{correlation}{specified code{'corSpatial'} object for the spatial correlation structure.}
item{coords}{matrix of unique coordinates for the measurement and grid sites.}
item{ztype}{grouping factor for the spatial variances.}
item{wmat}{matrix for mapping the random effects to the observed data.}
item{retype}{grouping factor for the random effects variances.}
item{control}{a list of control parameters used in the fitting process.}
}
references{
Yan, J., Cowles, M.K., Wang, S., and Armstrong, M. (2007) dQuote{Parallelizing MCMC for Bayesian Spatiotemporal Geostatistical Models}, Statistics and Computing, in press.
}
author{
Brian Smith email{brian-j-smith@uiowa.edu}, Jun Yan email{jun.yan@uconn.edu}, and Kate Cowles email{kate-cowles@uiowa.edu}}
seealso{
code{corClasses},
code{ramps.control},
code{mcmc},
code{DIC.ramps},
code{plot.ramps},
code{predict.ramps},
code{summary.ramps},
code{window.ramps}
}
examples{
## Load the included uranium datasets for use in this example
data(NURE)
## Geostatistical analysis of areal measurements
NURE.ctrl1 <- ramps.control(
iter = 25,
beta = param(0, "flat"),
sigma2.e = param(1, "invgamma", shape = 2.0, scale = 0.1, tuning = 0.75),
phi = param(10, "uniform", min = 0, max = 35, tuning = 0.50),
sigma2.z = param(1, "invgamma", shape = 2.0, scale = 0.1)
)
NURE.fit1 <- georamps(log(ppm) ~ 1,
correlation = corRExp(form = ~ lon + lat, metric = "haversine"),
weights = area,
data = NURE,
subset = (measurement == 1),
aggregate = list(grid = NURE.grid, blockid = "id"),
control = NURE.ctrl1
)
print(NURE.fit1)
summary(NURE.fit1)
## Analysis of point-source measurements
NURE.ctrl2 <- ramps.control(
iter = 25,
beta = param(0, "flat"),
sigma2.e = param(1, "invgamma", shape = 2.0, scale = 0.1, tuning = 0.75),
phi = param(10, "uniform", min = 0, max = 35, tuning = 0.5),
sigma2.z = param(1, "invgamma", shape = 2.0, scale = 0.1)
)
NURE.fit2 <- georamps(log(ppm) ~ 1,
correlation = corRExp(form = ~ lon + lat, metric = "haversine"),
data = NURE,
subset = (measurement == 2),
control = NURE.ctrl2
)
print(NURE.fit2)
summary(NURE.fit2)
## Joint analysis of areal and point-source measurements with
## prediction only at grid sites
NURE.ctrl <- ramps.control(
iter = 25,
beta = param(rep(0, 2), "flat"),
sigma2.e = param(rep(1, 2), "invgamma", shape = 2.0, scale = 0.1, tuning = 0.75),
phi = param(10, "uniform", min = 0, max = 35, tuning = 0.5),
sigma2.z = param(1, "invgamma", shape = 2.0, scale = 0.1),
z.monitor = NURE.grid
)
NURE.fit <- georamps(log(ppm) ~ factor(measurement) - 1,
correlation = corRExp(form = ~ lon + lat, metric = "haversine"),
variance = list(fixed = ~ measurement),
weights = area * (measurement == 1) + (measurement == 2),
data = NURE,
aggregate = list(grid = NURE.grid, blockid = "id"),
control = NURE.ctrl
)
print(NURE.fit)
summary(NURE.fit)
}
keyword{models}