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This is a R package to implement certain spatial and spatio-temporal models taking use to the cgeneric interface in the INLA package. This interface is a way to implement models by writing C code to build the precision matrix compiling it so that INLA can use it internally.
You can install the current CRAN version of INLAspacetime:
You can install the latest version of INLAspacetime from GitHub with
## install.packages("remotes")
remotes::install_github("eliaskrainski/INLAspacetime", build_vignettes=TRUE)some of the models presented in https://arxiv.org/abs/2006.04917
the barrier model proposed in https://doi.org/10.1016/j.spasta.2019.01.002
This is a basic example which fit a spacetime model for some fake data. The model fitting using inlabru facilitates coding.
set.seed(1)
n <- 5
dataf <- data.frame(
s1 = runif(n, -1, 1),
s2 = runif(n, -1, 1),
time = runif(n, 1, 4),
y = rnorm(n, 0, 1))
str(dataf)
#> 'data.frame': 5 obs. of 4 variables:
#> $ s1 : num -0.469 -0.256 0.146 0.816 -0.597
#> $ s2 : num 0.797 0.889 0.322 0.258 -0.876
#> $ time: num 1.62 1.53 3.06 2.15 3.31
#> $ y : num -0.00577 2.40465 0.76359 -0.79901 -1.14766Loading the packages:
library(INLA)
#> Loading required package: Matrix
#> Loading required package: foreach
#> Loading required package: parallel
#> Loading required package: sp
#> The legacy packages maptools, rgdal, and rgeos, underpinning this package
#> will retire shortly. Please refer to R-spatial evolution reports on
#> https://r-spatial.org/r/2023/05/15/evolution4.html for details.
#> This package is now running under evolution status 0
#> This is INLA_23.05.30-1 built 2023-05-30 11:52:19 UTC.
#> - See www.r-inla.org/contact-us for how to get help.
#> - To enable PARDISO sparse library; see inla.pardiso()
library(INLAspacetime)
library(inlabru)Define spatial and temporal discretization meshes
smesh <- inla.mesh.2d(
loc = cbind(0,0),
max.edge = 5,
offset = 2)
tmesh <- inla.mesh.1d(
loc = 0:5)Define the spacetime model object to be used
stmodel <- stModel.define(
smesh = smesh, ## spatial mesh
tmesh = tmesh, ## temporal mesh
model = '121', ## model, see the paper
control.priors = list(
prs = c(1, 0.1), ## P(spatial range < 1) = 0.1
prt = c(5, 0), ## temporal range fixed to 5
psigma = c(1, 0.1) ## P(sigma > 1) = 0.1
)
)Define the data model: the linear predictor terms
Setting the likelihood
ctrlf <- list(
hyper = list(
prec = list(
initial = 10,
fixed = TRUE)
)
)
datalike <- like(
formula = y ~ .,
family = "gaussian",
control.family = ctrlf,
data=dataf)Fitting
result <-
bru(
components = linpred,
datalike,
options = list(
control.inla = list(
int.strategy = "eb"
),
verbose = !TRUE)
)
#> Warning in inla.model.properties.generic(inla.trim.family(model), mm[names(mm) == : Model 'cgeneric' in section 'latent' is marked as 'experimental'; changes may appear at any time.
#> Use this model with extra care!!! Further warnings are disabled.Summary of the model parameters
result$summary.fixed
#> mean sd 0.025quant 0.5quant 0.975quant mode kld
#> Intercept 0.5211454 3.49482 -6.328575 0.5211454 7.370866 0.5211454 0
result$summary.hyperpar
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> Theta1 for field 1.202935 0.5125858 0.3321324 1.162519 2.324768 0.9707531
#> Theta2 for field 1.435746 0.1706775 1.1044643 1.434143 1.776487 1.4273398Please check it out at the Tutorials
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.