glgm-methods | R Documentation |
Fits a generalized linear geostatistical model or a log-Gaussian Cox process
using inla
## S4 method for signature 'ANY,ANY,ANY,ANY'
glgm(formula, data, grid, covariates, buffer=0, shape=1, prior, ...)
## S4 method for signature 'formula,SpatRaster,ANY,ANY'
glgm(formula, data, grid, covariates, buffer=0, shape=1, prior, ...)
## S4 method for signature 'formula,SpatVector,ANY,ANY'
glgm(formula, data, grid, covariates, buffer=0, shape=1, prior, ...)
## S4 method for signature 'formula,data.frame,SpatRaster,data.frame'
glgm(formula, data, grid, covariates, buffer=0, shape=1, prior, ...)
lgcp(formula=NULL, data, grid, covariates=NULL, border, ...)
data |
An object of class |
grid |
Either an integer giving the number of cells in the x direction, or a raster object which will be used for the spatial random effect. If the cells in the raster are not square, the resolution in the y direction will be adjusted to make it so. |
covariates |
Either a single raster, a list of rasters or a raster stack containing covariate values used when
making spatial predictions. Names of the raster layers or list elements correspond to names in the formula. If
a covariate is missing from the data object it will be extracted from the rasters. Defaults to |
formula |
Model formula, defaults to a linear combination of each of the layers in the |
prior |
list with elements named |
shape |
Shape parameter for the Matern correlation function, must be 1 or 2. |
buffer |
Extra space padded around the data bounding box to reduce edge effects. |
border |
boundary of the region on which an LGCP is defined, passed to |
... |
Additional options passed to
|
This function performs Bayesian inference for generalized linear geostatistical models with INLA. The Markov random field approximation on a regular lattice is used for the spatial random effect. The range parameter is the distance at which the correlation is 0.13, or
cov[U(s+h), U(s)] = (2^{1-\nu}/Gamma(\nu)) d^\nu besselK(d, \nu)
d= |h| \sqrt{8 \nu}/range
where \nu
is the shape parameter. The range parameter produced by glgm
multiplies the range parameter from INLA
by the cell size.
Elements of prior
can be named range
, sd
, or sdObs
. Elements can consist of:
a single value giving the prior median for penalized complexity priors (exponential on the sd or 1/range).
a vector c(u=a, alpha=b)
giving an quantile and probability for pc priors. For standard deviations alpha is an upper quantile, for the range parameter b = pr(1/range > 1/a).
a vector c(lower=a, upper=b)
giving a 0.025 and 0.975 quantiles for the sd or range.
a list of the form list(prior='loggamma', param=c(1,2))
passed directly to inla.
a two-column matrix of prior densities for the sd or range.
A list with two components named inla
, raster
, and parameters
. inla
contains the results of the call to the
inla
function. raster
is a raster stack with the following layers:
random. |
mean, sd, X0.0??quant: Posterior mean, standard deviation, and quantiles of the random effect |
predict. |
mean, sd, X0.0??quant: same for linear predictors, on the link scale |
predict.exp |
posterior mean of the exponential of the linear predictor |
predict.invlogit |
Only supplied if a binomial response variable was used. |
parameters
contains a list with elements:
summary |
a table with parameter estimates and posterior quantiles |
range , sd |
prior and posterior distributions of range and standard deviations |
## Not run:
# geostatistical model for the swiss rainfall data
if(requireNamespace("INLA", quietly=TRUE) ) {
INLA::inla.setOption(num.threads=2)
# not all versions of INLA support blas.num.threads
try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}
require("geostatsp")
data("swissRain")
swissRain = unwrap(swissRain)
swissAltitude = unwrap(swissAltitude)
swissBorder = unwrap(swissBorder)
swissRain$lograin = log(swissRain$rain)
swissFit = glgm(formula="lograin", data=swissRain,
grid=30,
covariates=swissAltitude, family="gaussian",
buffer=2000,
prior = list(sd=1, range=100*1000, sdObs = 2),
control.inla = list(strategy='gaussian')
)
if(!is.null(swissFit$parameters) ) {
swissExc = excProb(swissFit, threshold=log(25))
swissExcRE = excProb(swissFit$inla$marginals.random$space,
log(1.5),template=swissFit$raster)
swissFit$parameters$summary
matplot(
swissFit$parameters$range$postK[,'x'],
swissFit$parameters$range$postK[,c('y','prior')],
type="l", lty=1, xlim = c(0, 1000),
xlab = 'km', ylab='dens')
legend('topright', lty=1, col=1:2, legend=c('post','prior'))
plot(swissFit$raster[["predict.exp"]])
mycol = c("green","yellow","orange","red")
mybreaks = c(0, 0.2, 0.8, 0.95, 1)
plot(swissBorder)
plot(swissExc, breaks=mybreaks, col=mycol,add=TRUE,legend=FALSE)
plot(swissBorder, add=TRUE)
legend("topleft",legend=mybreaks, fill=c(NA,mycol))
plot(swissBorder)
plot(swissExcRE, breaks=mybreaks, col=mycol,add=TRUE,legend=FALSE)
plot(swissBorder, add=TRUE)
legend("topleft",legend=mybreaks, fill=c(NA,mycol))
}
# a log-Gaussian Cox process example
myPoints = vect(cbind(rbeta(100,2,2), rbeta(100,3,4)))
mycov = rast(matrix(rbinom(100, 1, 0.5), 10, 10), extent=ext(0, 1, 0, 1))
names(mycov)="x1"
if(requireNamespace("INLA", quietly=TRUE) ) {
INLA::inla.setOption(num.threads=2)
# not all versions of INLA support blas.num.threads
try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}
res = lgcp(
formula=~factor(x1),
data=myPoints,
grid=squareRaster(ext(0,1,0,1), 20), covariates=mycov,
prior=list(sd=c(0.9, 1.1), range=c(0.4, 0.41),
control.inla = list(strategy='gaussian'), verbose=TRUE)
)
if(length(res$parameters)) {
plot(res$raster[["predict.exp"]])
plot(myPoints,add=TRUE,col="#0000FF30",cex=0.5)
}
## End(Not run)
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